Deep Learning for Medical Image-Based Cancer Diagnosis
Abstract
:Simple Summary
Abstract
1. Introduction
- (i)
- The principle and application of radiological and histopathological images in cancer diagnosis are introduced in detail;
- (ii)
- This paper introduces 9 basic architectures of deep learning, 12 classical pretrained models, and 5 typical methods to overcome overfitting. In addition, advanced deep neural networks are introduced, such as vision transformers, transfer learning, ensemble learning, graph neural network, and explainable deep neural networks;
- (iii)
- The application of deep learning technology in medical imaging cancer diagnosis is deeply analyzed, including image classification, image reconstruction, image detection, image segmentation, image registration, and image fusion;
- (iv)
- The current challenges and future research hotspots are discussed and analyzed around data, labels, and models.
2. Common Imaging Techniques
2.1. Computed Tomography
2.2. Magnetic Resonance Imaging
MRI | Description |
---|---|
Conception |
|
Feature |
|
2.3. Ultrasound
Ultrasound | Description |
---|---|
Conception |
|
Feature |
|
2.4. X-ray
X-ray | Description |
---|---|
Conception |
|
Feature |
|
2.5. Positron Emission Tomography
PET | Description |
---|---|
Conception |
|
Feature |
|
2.6. Histopathology
3. Deep Learning
3.1. Basic Model
3.1.1. Convolutional Neural Network
3.1.2. Fully Convolutional Network
3.1.3. Autoencoder
Author | Method | Year | Description | Feature |
---|---|---|---|---|
Bengio et al. [159] | SAE 1 | 2007 | Use layer-wise training to learn network parameters. | The pre-trained network fits the structure of the training data to a certain extent, which makes the initial value of the entire network in a suitable state, which is convenient for the supervised stage to speed up the iterative convergence. |
Vincent et al. [160] | DAE 2 | 2008 | Add random noise perturbation to the input data. | Representation reconstructs high-level information from chaotic information, allowing high learning capacity while preventing learning a useless identity function in the encoder and decoder, improving algorithm robustness, and obtaining a more efficient representation of the input. |
Vincent et al. [152] | SDAE 3 | 2010 | Multiple DAEs are stacked together to form a deep architecture. The input is corroded (noised) only during training; once the training is complete, there is no need to corrode. | It has strong feature extraction ability and good robustness. It is just a feature extractor and does not have a classification function. |
Ng [151] | Sparse autoencoder | 2011 | A regular term controlling sparsity is added to the original loss function. | Features can be automatically learned from unlabeled data and better feature descriptions can be given than the original data. |
Rifai et al. [154] | CAE 4 | 2011 | The autoencoder object function is constrained by the encoder’s Jacobian matrix norm so that the encoder can learn abstract features with anti-jamming. | It mainly mines the inherent characteristics of the training samples, which entails using the gradient information of the samples themselves. |
Masci et al. [161] | Convolutional autoencoder | 2011 | Utilizes the unsupervised learning method of the traditional autoencoder, combining the convolution and pooling operations of the convolutional neural network. | Through the convolution operation, the convolutional autoencoder can well preserve the spatial information of the two-dimensional signal. |
Kingma et al. [153] | VAE 5 | 2013 | Addresses the problem of non-regularized latent spaces in autoencoders and provides generative capabilities for the entire space. | It is probabilistic and the output is contingent; new instances that look like input data can be generated. |
Srivastava et al. [162] | Dropout autoencoder | 2014 | Reduce the expressive power of the network and prevent overfitting by randomly disconnecting the network. | The degree of overfitting can be reduced and the training time is long. |
Srivastava et al. [163] | LAE 6 | 2015 | Compressive representations of sequence data can be learned. | Representation helps improve classification accuracy, especially when there are few training examples. |
Makhzani et al. [164] | AAE 7 | 2015 | An additional discriminator network is used to determine whether hidden variables of dimensionality reduction are sampled from prior distributions. | Minimize the reconstruction error of traditional autoencoders; match the aggregated posterior distribution of the latent variables of the autoencoder with an arbitrary prior distribution. |
Xu et al. [155] | SSAE 8 | 2015 | Advanced feature representations of pixel intensity can be captured in an unsupervised manner. | Only advanced features are learned from pixel intensity to identify the distinguishing features of the kernel; efficient coding can be achieved. |
Higgins et al. [165] | beta-VAE | 2017 | beta-VAE is a generalization of VAE that only changes the ratio between reconstruction loss and divergence loss. The scalar β denotes the influence factor of the divergence loss. | The potential channel capacity and independence constraints can be balanced with the reconstruction accuracy. Training is stable, makes few assumptions about the data, and relies on tuning a single hyperparameter. |
Zhao et al. [166] | info-VAE | 2017 | The ELBO objective is modified to address issues where variational autoencoders cannot perform amortized inference or learn meaningful latent features. | Significantly improves the quality of variational posteriors and allows the efficient use of latent features. |
Van Den Oord et al. [167] | vq-VAE 9 | 2017 | Combining VAEs with vector quantization for discrete latent representations. | Encoder networks output discrete rather than continuous codes; priors are learned rather than static. |
Dupont [168] | Joint-VAE | 2018 | Augment the continuous latent distribution of a variational autoencoder using a relaxed discrete distribution and control the amount of information encoded in each latent unit. | Stable training and large sample diversity, modeling complex continuous and discrete generative factors. |
Kim et al. [169] | factorVAE | 2018 | The algorithm motivates the distribution of the representation so that it becomes factorized and independent in the whole dimension. | It outperforms β-VAE in disentanglement and reconstruction. |
3.1.4. Deep Convolutional Extreme Learning Machine
3.1.5. Recurrent Neural Network
3.1.6. Long Short-Term Memory
3.1.7. Generative Adversarial Network
3.1.8. Deep Belief Network
3.1.9. Deep Boltzmann Machine
3.2. Classical Pretrained Model
3.2.1. LeNet-5
3.2.2. AlexNet
3.2.3. ZF-Net
3.2.4. VGGNet
3.2.5. GoogLeNet
3.2.6. ResNet
3.2.7. DenseNet
3.2.8. MobileNet
3.2.9. ShuffleNet
3.2.10. SqueezeNet
3.2.11. XceptionNet
3.2.12. U-net
3.3. Advanced Deep Neural Network
3.3.1. Transfer Learning
- (i)
- Instance-based transfer learning entails reusing part of the data in the source domain through the heavy weight method of the target domain learning;
- (ii)
- Feature-representation transfer learning aims to learn a good feature representation through the source domain, encode knowledge in the form of features, and transfer it from source domain to the target domain for improving the effect of target domain tasks. Feature-based transfer learning is based on the assumption that the target and source domains share some overlapping common features. In feature-based methods, a feature transformation strategy is usually adopted to transform each original feature into a new feature representation for knowledge transfer [279];
- (iii)
- Parameter-transfer learning means that the tasks of the target domain and source domain share the same model parameters or obey the same prior distribution. It is based on the assumption that individual models for related tasks should share a prior distribution of some parameters or hyperparameters. Generally, there are usually two specific ways to achieve this. One is to initialize a new model with the parameters of the source model and then fine-tune it. Secondly, the source model or some layers in the source model are solidified as feature extractors in the new model. Then an output layer is added for the target problem and learning on this basis can effectively utilize previous knowledge and reduce training costs [274];
- (iv)
- Relational-knowledge transfer learning involves knowledge transfer between related domains, which needs to assume that the source and target domains are similar and can share some logical relationship, and attempts to transfer the logical relationship among data from the source domain to the target domain.
3.3.2. Ensemble Learning
3.3.3. Graph Neural Network
3.3.4. Explainable Deep Neural Network
3.3.5. Vision Transformer
- (i)
- The image of H × W × C is changed into a sequence of N × (P2 × C), where P is the size of the image block. This sequence can be viewed as a series of flattened image patches. That is, the image is cut into small patches and then flattened. The sequence contains a total of N = H × W/P2 image patches and the dimension of each image patch is (P2 × C). After the above transformation, N can be regarded as the length of the sequence;
- (ii)
- Since the dimension of each image patch is (P2 × C) and the vector dimension we actually need is D, we also need to Embed the image patch. That is, each image patch will be linearly transformed and the dimension will be compressed to D.
3.4. Overfitting Prevention Technique
3.4.1. Batch Normalization
3.4.2. Dropout
3.4.3. Weight Initialization
3.4.4. Data Augmentation
4. Application of Deep Learning in Cancer Diagnoses
4.1. Image Classification
4.2. Image Detection
4.3. Image Segmentation
4.4. Image Registration
4.5. Image Reconstruction
4.6. Image Synthesis
5. Discussion
5.1. Data
5.1.1. Less Training Data
5.1.2. Class Imbalance
5.1.3. Image Fusion
5.2. Label
5.2.1. Insufficient Annotation Data
5.2.2. Noisy Labels
5.2.3. Supervised Paradigm
5.3. Model
5.3.1. Model Explainability
5.3.2. Model Robustness and Generalization
5.4. Radiomics
6. Conclusions
6.1. Limitations and Challenges
- (i)
- Datasets problems.
- (ii)
- The model lacks explainability.
- (iii)
- Poor generalization ability.
- (iv)
- Lack of high-performance models for multi-modal images.
6.2. Future Research Directions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Name | Medium | Imaging Method | Imaging Basis | Features | Advantages | Disadvantages | Radiation | Use Cases |
---|---|---|---|---|---|---|---|---|
MRI | Magnetic fields and radio waves | Mathematical reconstruction | A variety of parameters | Tomographic images, multi-parameter, multi-sequence imaging, rich grayscale information. | High soft-tissue resolution. | Long examination time, prone to motion artifacts, low spatial resolution, not suitable for patients with metal parts, high price. | No | soft tissue [23], nervous tissue [24], internal organs [25], etc. |
X-ray | Ionizing radiation | Transmission projection | Density and thickness | Strong penetrability, wide dynamic range, suitable for image diagnosis with small grayscale differences. | Full picture, real-time, fine image, low cost. | Limited density resolution, overlapping images, poor identification of soft tissues. | Yes | Skeletal system [26], gastrointestinal tract [27], cardiovascular angiography and dynamic observation [28,29], etc. |
CT | Ionizing radiation | Mathematical reconstruction | Absorption coefficient | Tomographic image, grayscale image, higher grayscale, can display the tissue density of the human body section. | Fast imaging speed, high-density resolution, no image overlap, further quantitative analysis. | Low spatial resolution, artifacts, and partial volume effects, reflecting only anatomical features. | Yes | Bones [30], lungs, internal organs [31], angiography [32,33], etc. |
US | Sound waves | Mathematical reconstruction | Acoustic impedance interface | Suitable for moderate acoustic tissue measurements (soft tissue, muscle, etc.) and imaging of human anatomy and blood flow. | Safe and reliable, no radiation, low cost, can detect very subtle diseased tissue, real-time dynamic imaging. | Poor image contrast, limited field of view, difficult in displaying normal tissue and large lesions. | No | Abdominal organs [34], heart [35,36], ophthalmology [37], obstetrics and gynecology [38], etc. |
PET | Radioactive tracer | Mathematical reconstruction | Using positron radionuclide labeling | Concentration image of positrons, showing biological metabolic activity. | High sensitivity, high specificity, whole-body imaging, accurate location can achieve the purpose of early diagnosis. | Low image clarity, poor specificity for inflammation, expensive, the examiner needs to have rich experience. | Yes | Brain blood flow [39], metabolic activity [40], cancer diagnosis [41], etc. |
CT | Description |
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Conception |
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Feature |
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Histopathological Image | Description |
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Conception |
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Feature |
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Name | Brief Description | Basic Module |
---|---|---|
CNN [129] | Feed-forward neural network with convolutional computation and deep structure. | It consists of an input layer, an output layer, and multiple hidden layers. The hidden layers can be divided into convolutional layers, pooling layers, RELU, and fully connected layers. |
FCN [130] | Pixel-level classification of images solves the problem of semantic-level image segmentation. | All layers in the model network are convolutional layers. |
AE [131] | Efficient feature extraction and feature representation for high-dimensional data using unsupervised learning. | Encoder and decoder. |
DC-ELM [132] | Combining the feature abstraction performance of convolutional neural networks and the fast training of extreme learning machines | It consists of an input layer, an output layer, and multiple alternating convolutional and pooling layers. |
RNN [133] | A neural network with short-term memory is often used to process time-series data. | It consists of an input layer, a recurrently connected hidden layer, and an output layer. |
LTSM [134] | A special kind of RNN capable of learning long dependencies. | A cell, an input gate, an output gate, and a forget gate. |
GAN [135] | A deep generative model based on adversarial learning. | Generator and discriminator. |
DBN [136] | The number of layers is increased by stacking multiple RBMs to increase expressive power. | Multi-layer RBM. |
DBM [137] | A stack of multi-layer RBMs. The middle layer is bidirectionally connected to the adjacent layer. | Boltzmann distribution. |
Name | Year | Brief Description |
---|---|---|
LeNet-5 [129] | 1998 | It was designed to solve the problem of handwritten digit recognition and is considered one of the pioneering works of CNN. |
AlexNet [208] | 2012 | The first deep convolutional neural network structure on large-scale image datasets. |
ZF-Net [209] | 2014 | Visualize and understand convolutional networks. |
VGGNet [210] | 2014 | Deeper architecture and simpler form. |
GoogLeNet [211] | 2014 | The introduced Inception combines feature information of different scales to obtain better feature representation. |
U-net [212] | 2015 | Convolutional networks for biomedical image segmentation. |
ResNet [213] | 2016 | The internal residual block uses a skip connection which alleviates the gradient disappearance problem caused by increasing the depth in the deep neural network. |
MobileNet [214] | 2016 | Lightweight CNN for mobile vision problems. |
SqueezeNet [215] | 2016 | Use the fire module to compress parameters. |
DarkNet [216] | 2016 | An open source neural network framework written in C and CUDA. |
DenseNet [217] | 2017 | Re-usage of feature maps. |
XceptionNet [218] | 2017 | Better performed than Inception-v3. |
Inception-ResNet [219] | 2017 | Residual connections were used to increase the training speed of Inception networks. |
ShuffleNet [220] | 2018 | Lightweight CNN using pointwise group convolution and channel shuffle. |
NasNet [221] | 2018 | An architectural building block is searched on a small dataset and then transferred to a larger dataset. |
EfficientNet [222] | 2019 | A compound scaling method with extremely high parametric efficiency and speed. |
Brief Description | Description | Typical Representative |
---|---|---|
Dense connection mechanism | There is a direct connection between any two layers. | Dense U-Net [253]; Denseblock U-Net [254] |
Residual connection mechanism | The convolution layer of U-Net is replaced with a residual block. The skip connection uses a residual connection path. The encoder and decoder are replaced by a residual network. | Residual U-Net [255]; RRA-UNet [256] |
Multi-scale mechanism | Images of multiple scales are input and the results are fused so that the final output combines the features from receptive fields of different sizes. | MU-Net [257]; MDFA-Net [258] |
Ensemble mechanism | A group of neural networks processes the same input data in parallel and then combines their outputs to complete the segmentation. | AssemblyNet [259]; DIU-Net [260] |
Dilated mechanism | The dilated convolution is used in the encoder and decoder to increase the size of the small convolution kernel while keeping the parameter amount of the convolution unchanged. | DiSegNet [261]; DIN [262] |
Attention mechanism | Attention modules are added to the encoder, decoder, and skip connections. | ANU-Net [263]; SA-UNet [264] |
Transformer mechanism | The operation of the encoder, decoder, and jump connection is changed to transformer. | TA-Net [265]; FAT-Net [266] |
Author | Variation | Year | Features |
---|---|---|---|
Ronneberger et al. [212] | U-Net | 2015 | It consists of a contracting path and an expanding path, which are used to obtain contextual information and precise localization, respectively. These two paths are symmetrical to each other. |
Çiçek et al. [267] | 3D U-Net | 2016 | The core structure still contains a contracting path and a symmetric expanding path. Overall, 3D volume segmentation is supported and all 2D operations are replaced by corresponding 3D operations, resulting in 3D segmented images that can be segmented with minimal annotation examples. |
Oktay et al. [268] | Attention U-Net | 2018 | It is a hybrid structure that uses attention gates to focus on specific objects of importance and each level in the expansion path has an attention gate through which corresponding features from the contraction path must pass. |
Alom et al. [269] | R2U-Net 1 | 2018 | It builds on residual and loop techniques, using a simpler concatenation of functions from the encoder to decoder. |
Zhou et al. [270] | U-Net++ | 2018 | It is essentially a deeply supervised encoder–decoder network, where the encoder and decoder subnetworks are connected by a series of nested, dense skip pathways, aiming to reduce the semantic gap between feature maps. |
Khanna et al. [255] | Residual U-Net | 2020 | Residuals are integrated into the contraction path of U-Net networks, thus reducing the computational burden and avoiding network degradation problems. |
Ibtehaz et al. [271] | MultiResUNet | 2020 | Res paths are proposed to reconcile two incompatible sets of features. The two feature mappings are more uniform. MultiRes blocks are proposed to augment U-Net’s multiresolution analysis capabilities. It is lightweight and requires less memory. |
Li et al. [263] | ANU-Net 2 | 2020 | A redesigned dense skip connection and attention mechanism are introduced and a new hybrid loss function is designed. |
Zhang et al. [260] | DIU-Net 3 | 2020 | The Inception-Res module and the densely connecting convolutional module are integrated into the U-net structure. |
Yeung et al. [272] | Focus U-Net | 2021 | Efficient spatial and channel attention are combined into Focus Gate. Focus Gate uses an adjustable focal parameter to control the degree of background suppression. Short-range skip connections and deep supervision are added. Hybrid focal loss is used to deal with class-imbalanced image segmentation. |
Beeche et al. [273] | Super U-Net | 2022 | In the classical U-Net architecture, a dynamic receiving field module and a fusion up-sampling module are integrated. Image segmentation performance can be improved significantly by dynamic receptive field and fusion up sampling. |
Author | Datasets | IoU (%) | Dice (%) | Precision (%) | Recall (%) | DSC 1 (%) | JI 2 (%) | Body Part or Organ |
---|---|---|---|---|---|---|---|---|
Ronneberger et al. [212] | LiTS | 89.49 | 94.45 | 93.24 | 95.70 | - | - | Liver |
CHAOS | 84.46 | 91.58 | 89.20 | 94.08 | - | - | Kidney | |
CHAOS | 76.18 | 86.48 | 82.34 | 91.06 | - | - | Spleen | |
CHAOS | 75.37 | 85.96 | 87.31 | 84.65 | - | - | Liver | |
Çiçek et al. [267] | Private dataset | 86.30 | - | - | - | - | - | The Xenopus kidney |
Oktay et al. [268] | LiTS | 93.39 | 96.58 | 96.79 | 96.37 | - | - | Liver |
CHAOS | 85.77 | 92.34 | 90.97 | 93.76 | - | - | Kidney | |
CHAOS | 84.13 | 91.38 | 91.54 | 91.22 | - | - | Spleen | |
CHAOS | 76.00 | 86.37 | 91.11 | 82.09 | - | - | Liver | |
Alom et al. [269] | LiTS | 90.69 | 95.11 | 93.80 | 96.48 | - | - | Liver |
CHAOS | 85.54 | 92.21 | 91.92 | 92.50 | - | - | Kidney | |
CHAOS | 81.50 | 89.77 | 93.60 | 86.24 | - | - | Spleen | |
CHAOS | 77.80 | 87.50 | 92.11 | 83.39 | - | - | Liver | |
Khanna et al. [255] | LUNA16 | - | - | - | 98.61 ± 0.14 | 98.63 ± 0.05 | 97.32± 0.10 | Lung |
VESSEL12 | - | - | - | 99.61 ± 0.01 | 99.62 ± 0.003 | 99.24 ± 0.007 | Lung | |
HUG-ILD | - | - | - | 98.73 ± 0.001 | 98.68 ± 0.04 | 97.39 + 0.06 | Lung | |
Zhou et al. [270] | LiTS | 94.46 | 97.15 | 98.16 | 96.17 | - | - | Liver |
CHAOS | 86.58 | 92.81 | 90.87 | 94.82 | - | - | Kidney | |
CHAOS | 81.05 | 89.53 | 86.37 | 92.93 | - | - | Spleen | |
CHAOS | 84.23 | 91.39 | 93.06 | 89.79 | - | - | Liver | |
Yeung et al. [272] | Kvasir-SEG | 0.845 (mIoU) | - | 91.70 | 91.60 | 0.910(mDSC) | - | colorectum |
CVC-ClinicDB | 0.893 (mIoU) | - | 93.00 | 95.60 | 0.941(mDSC) | - | colorectum | |
Ibtehaz et al. [271] | ISIC-2018 | - | - | - | - | - | 80.2988 ± 0.3717 | Skin lesions |
CVC-ClinicDB | - | - | - | - | - | 82.0574 ± 1.5953 | Colon | |
FMID 3 | - | - | - | - | - | 91.6537 ± 0.9563 | U2OS cells; NIH3T3 cells | |
BraTS17 | - | - | - | - | - | 78.1936 ± 0.7868 | Glioblastoma; lower grade glioma | |
Li et al. [263] | LiTS | 97.48 | 98.15 | 98.15 | 99.31 | - | - | Liver |
CHAOS | 90.10 | 94.79 | 94.00 | 95.60 | - | - | Kidney | |
CHAOS | 89.23 | 94.31 | 95.19 | 93.44 | - | - | Spleen | |
CHAOS | 87.89 | 93.55 | 94.23 | 92.88 | - | - | Liver | |
Zhang et al. [260] | KDSB2017 4 | - | 98.57 | - | - | - | - | Lung |
DRIVE + STARE + CHASH_DB1 | - | 95.82 | - | - | - | - | Blood vessel | |
MICCAI BraTS 2017 | - | 98.92 | - | - | - | - | Brain tumor | |
Beeche et al. [273] | DRIVE (n = 40) | - | - | - | - | 80.80 ± 0.021 | - | Retinal vessels |
Kvasir-SEG (n = 1000) | - | - | - | - | 80.40 ± 0.239 | - | GI polyps | |
CHASE DB1 (n = 28) | - | - | - | - | 75.20 ± 0.019 | - | Retinal vessels | |
ISIC (n = 200) | - | - | - | - | 87.70 ± 0.135 | - | Skin lesions |
Method | Description | Typical Method | Features |
---|---|---|---|
Instance | Samples from different source tasks are collected and reused for learning of the target task. | TrAdaBoost | The method is simple, easy to implement, unstable, and more empirical. |
Feature-representation | By introducing the source data features to help complete the learning task of the target data feature domain, the features of the source domain and the target domain are transformed into the same space through feature transformation. | Self-taught learning, multi-task structure learning | Applicable to most methods, the effect is better, it is difficult to solve, and it is prone to overfitting. |
Parameter | When some parameters are shared between the source task and the target task, or the prior distribution of model hyperparameters is shared, the model of the source domain is transferred to the target domain. | Learning to learn, Regularized multi-task learning | The similarity between the models can be fully utilized and the model parameters are not easy to converge. |
Relational-knowledge | It facilitates learning tasks on target data by mining relational patterns relevant to the target data from the source domain. | Mapping | Compatible for data with dependency and identical distribution |
Explanation Type | Characteristics |
---|---|
Local | Provide explanations for individual samples. |
Global | Provide explanations for a set of samples or the entire model. |
Data Modality Specific | Explanation methods that apply only to specific data types. |
Data Modality Agnostic | Explanation methods are applicable to any data type. |
Ad-Hoc | The model itself is designed to be inherently explainable. |
Post-Hoc | Provide explanations after classification is performed. |
Model Agnostic | Can explain any model and is not limited to a certain type. |
Model Specific | Only available on certain models. |
Attribution | Attempts to compute the most important neural network inputs relative to the network result. |
Non-Attribution | Develop and validate an explainability method for a given specialized problem. |
Method | Year | Goal | Description | Features |
---|---|---|---|---|
Xavier initialization [323] | 2010 | Solve the problem of gradient disappearance or gradient explosion that may be caused by random initialization. | The range of weight initialization is determined according to the number of input neurons in the previous layer and the number of output neurons in the next layer. | It reduces the probability of gradient vanishing/exploding problems. The influence of the activation function on the output data distribution is not considered and the ReLU activation function does not perform well. |
Orthogonal Initialization [324] | 2013 | Orthogonalize the weight matrix. | It solves the problem of gradient disappearance and gradient explosion under the deep network and is often used in RNN. | It can effectively reduce the redundancy and overfitting in the neural network and improve the generalization ability and performance of the network. Computational complexity is high, so it may not be suitable for large neural networks. |
He initialization [325] | 2015 | The input and output data have the same variance; suitable for neural networks using the ReLU activation function. | In the ReLU network, it is assumed that half of the neurons in each layer are activated and the other half is 0, just divide by 2 on the basis of Xavier to keep the variance constant. | Simple and effective, especially suitable for the case where the activation function is ReLU. Compared with the Xavier initialization, it can effectively improve the training speed and performance of the network. In some cases, it may cause the weight to be too small or too large, thus affecting the network performance. |
Data-dependent Initialization [326] | 2015 | Focus on behavior on smaller training sets, handle structured initialization, and improve pretrained networks. | It relies on the initialization process of the data. All units in the network train at roughly the same rate by setting the network’s weights. | CNN representations for tasks with limited labeled data are significantly improved and representations learned by self-supervised and unsupervised methods are improved. Early training of CNNs on large-scale datasets is greatly accelerated. |
LSUV [327] | 2015 | Produces thin and very deep neural networks. | The weights of each convolutional or inner product layer are pre-initialized with an orthogonal matrix. The variance in the output of each layer is normalized to be equal to 1 from the first layer to the last layer. | It has minimal computation and very low computational overhead. Due to variability in the data, it is often not possible to normalize the variance with the required precision. |
Sparse initialization [328] | 2017 | Achieving sparsity. | The weights are all initialized to 0. Some parameters are chosen randomly with some random values. | The parameters occupy less memory. Redundancy and overfitting in neural networks can be reduced. The generalization ability and performance of the network are improved. Some elements in the weight matrix may be too large or too small, thereby affecting the performance of the network. |
Fixup [329] | 2019 | For networks with residual branches. | Standard initialization is rescaled appropriately. | Deep residual networks can be reliably trained without normalization. |
ZerO Initialization [330] | 2021 | Deterministic weight initialization. | It is based on the identity transformation and the Hadamard transformation and only initializes the weights of the network with 0 and 1 (up to a normalization factor). | Ultra-deep networks without batch normalization are trained. It has obvious characteristics of low-rank learning and solves the problem of training decay. The trained model is more reproducible. |
Data Augmentation Category | Advantages | Disadvantages |
---|---|---|
Geometric transformations | It is simple and easy to implement, which can increase the spatial geometry information of the data set and improve the robustness of the model in different perspectives and positions. | The amount of added information is limited. The data is repeatedly memorized. Inappropriate operations may change the original semantic annotation of the image. |
Color space | The method is simple and easy to implement. The color information of the dataset is added to improve the robustness of the model under different lighting conditions. | The amount of added information is limited and repeated memory of the data may change the important color information in the image. |
Kernel filter | It can improve the robustness of the model to motion blur and highlight the details of objects. | It is implemented by filtering, which is repeated with the internal mechanism of CNN. |
Image Erasing | It can increase the robustness of the model under occlusion conditions, enable the model to learn more descriptive features in the image, and pay attention to the global information of the entire image. | The semantic information of the original image may be tampered with. Images may not be recognized after important partial information is erased. |
Mixing images | The pixel value information of multiple images is mixed. | Lack of interpretability |
Noise injection | This method enhances the filtering ability of the model to noise interference and redundant information and improves the recognition ability of the model of different quality images. | Unable to add new valid information. The improvement effect on model accuracy is not obvious. |
Feature space augmentation | The feature information of multiple images is fused. | Vector data is difficult to interpret. |
Adversarial training [339] | It can improve the weak links in the learned decision boundary and improve the robustness of the model. | Extremely slow and inaccurate. More data, deeper and more complex models are required. |
GAN-based Data Augmentation | Sampling from the fitted data distribution generates an unlimited number of samples. | A certain number of training samples are required to train the GAN model, which is difficult to train and requires extra model training costs. In most cases, the quality of the generated images is difficult to guarantee and the generated samples cannot be treated as real samples. |
Neural style transfer | It can realize mutual conversion between images of the same content and different modalities and can help solve special problems in many fields. | Two datasets from different domains need to be constructed to train the style transfer model, which requires additional training overhead. |
Meta learning data augmentations | Neural network is used to replace the definite data augmentation method to train the model to learn better augmentation strategies. | Introducing additional networks requires additional training overhead. |
Reinforcement learning data augmentation | Combining existing data augmentation methods to search for the optimal strategy. | The policy search space is large, the training complexity is high, and the calculation overhead is large. |
Data Augmentation Category | Method | Year | Describe | M/D |
---|---|---|---|---|
Geometric transformations | Flipping | - | Usually, the image flip operation is performed about the horizontal or vertical axis. | M |
Rotating | - | Select an angle and rotate the image left or right to change the orientation of the image content. | M | |
Zoom | - | The image is enlarged and reduced according to a certain ratio without changing the content in the image. | M | |
shearing | - | Move part of the image in one direction and another part in the opposite direction. | M | |
translating, | - | Shifting the image left, right, up, or down avoids positional bias in the data. | M | |
skew | - | Perspective transforms. | M | |
Cropping | - | It is divided into uniform cropping and random cropping. Uniform cropping crops images of different sizes to a set size. Random cropping is similar to translation, the difference is that translation retains the original image size and cropping reduces the size. | M | |
Color space | Color jittering [213] | 2016 | Randomly change the brightness, contrast, and saturation of the image. | M |
PCA jittering [340] | 2017 | Principal component analysis is carried out on the image to obtain the principal component and then the principal component is added to the original image by Gaussian disturbance with mean of 0 and variance of 0.1 to generate a new image. | M | |
Kernel filter | Gaussian blur filter [341] | 1991 | The image is blurred using Gaussian blur. | M |
edge filter [342] | 1986 | Get an image with edge sharpening, highlighting more details of the object. | M | |
PatchShuffle [343] | 2017 | Rich local variations are created by generating images and feature maps with internally disordered patches. | M | |
Image Erasing | Random erasing [344] | 2020 | During training, a rectangular region in the image is randomly selected and its pixels are erased with random values. | M |
Cutout [345] | 2017 | Randomly mask input square regions during training. | M | |
HaS [346] | 2017 | Patches are hidden randomly in training images. When the most discriminative parts are hidden, forcing the network to find other relevant parts. | M | |
GridMask [347] | 2020 | Based on the deletion of regions in the input image, the deleted regions are a set of spatially uniformly distributed squares that can be controlled in terms of density and size. | M | |
FenceMask [348] | 2020 | The “ simulation of object occlusion” strategy is employed to achieve a balance between the information preservation of input data and object occlusion. | M | |
Mixing images | Mixup [349] | 2017 | The neural network is trained on convex combinations of pairs of examples and their labels. | M |
SamplePairing [350] | 2018 | Another image randomly selected from the training data is superimposed on one image to synthesize a new sample, that is, the average value of the two images is taken for each pixel. | M | |
Between-Class Learning [351] | 2018 | Two images belonging to different classes are mixed in a random ratio to generate between-class images. | M | |
CutMix [352] | 2019 | Patches were cut and pasted among the training images, where the ground truth labels was also mixed in proportion to the area of the patches. | M | |
AugMix [353] | 2019 | Using random and diverse augmentation, Jensen–Shannon divergence consistency loss, and mixing multiple augmented images | M | |
Manifold Mixup [354] | 2019 | Using semantic interpolation as an additional training signal, neural networks with smoother decision boundaries at multiple representation levels are obtained. | M | |
Fmix [355] | 2020 | A mixed sample data augmentation using a random binary mask obtained by applying thresholds to low-frequency images sampled from Fourier space. | M | |
SmoothMix [356] | 2020 | Image blending is conducted based on soft edges and training labels are computed accordingly. | M | |
Deepmix [357] | 2021 | Takes embeddings of historical samples as input and generates augmented embeddings online. | M | |
SalfMix [358] | 2021 | A data augmentation method for generating saliency map-based self-mixing images. | M | |
Noise injection | forward noise adjustment scheme [359] | 2018 | Insert random values into an image to create a new image. | M |
DisturbLabel [360] | 2016 | Randomly replace the labels of some samples during training and apply disturbance to the sample labels, which is equivalent to adding noise at the loss level. | M | |
Feature space augmentation | Dataset Augmentation in Feature Space [361] | 2017 | Representation is first learned using encoder–decoder algorithm and then different transformations are applied to the representation, such as adding noise, interpolation, or extrapolation. | D |
Feature Space Augmentation for Long-Tailed Data [362] | 2020 | Use features learned from the classes with sufficient samples to augment underrepresented classes in the feature space. | D | |
SMOTE [363] | 2002 | Interpolate on the feature space to generate new samples. | D | |
FeatMatch [364] | 2020 | A learned feature-based refinement and augmentation method that exploits information from within-class and across-class representations extracted through clustering to produce various complex sets of transformations. | D | |
Adversarial training [339] | FGSM [365] | 2014 | This method believes that the attack is to add disturbance to increase the loss of the model and it should be best to generate attack samples along the gradient direction. It is a one-time attack. That is, adding a gradient to a graph only increases the gradient once. | D |
PGD [366] | 2017 | As the strongest first-order adversary, it is an efficient solution to the internal maximization problem. | D | |
FGSM+ random initialization [367] | 2020 | Eric Wong, Leslie Rice, and J. Zico Kolter. Fast is better than free: Revisiting adversarial training. In ICLR, 2020. Adversarial training using FGSM, combined with random initialization, is as effective as PGD-based training, but at a much lower cost. | D | |
GradAlign [368] | 2020 | Catastrophic overfitting is prevented by explicitly maximizing the gradient alignment inside the perturbation set. | D | |
Fast C and W [369] | 2021 | An accelerated SAR-TR AA algorithm. A network of trained deep encoder replaces the process of iteratively searching for the best perturbation of the input SAR image in the vanilla C and W algorithm. | D | |
GAN-based Data Augmentation | GAN [194] | 2014 | sing GAN generative models to generate more data can be used as an oversampling technique to address class imbalance. | D |
CGAN [370] | 2014 | Add some constraints to GAN to control the generation of images. | ||
DCGAN [371] | 2015 | Combining CNN with GAN, deep convolutional adversarial pair learns representation hierarchies from object parts to scenes in the generator and discriminator. | D | |
LapGAN [372] | 2015 | Images are generated in a coarse-to-fine fashion using a cascade of convolutional networks within the Laplacian Pyramid framework. | D | |
InfoGAN [373] | 2016 | Interpretable representation learning in a completely unsupervised manner via information-maximizing GANs. | D | |
EBGAN [374] | 2016 | An energy-based generative adversarial network model in which the discriminator is treated as a function of energy. | D | |
WGAN [375] | 2017 | The loss function is derived by means of earth mover or Wasserstein distance. | D | |
BEGAN [376] | 2017 | The generator and discriminator are balanced for training an autoencoder-based generative adversarial network. | D | |
PGGAN [377] | 2017 | Generators and discriminators are progressively increasing. | D | |
BigGAN [378] | 2018 | Large-scale GAN training for high-fidelity natural image synthesis based on GAN architecture. | D | |
StyleGAN [379] | 2019 | A style-based GAN generator architecture controls the image synthesis process. | D | |
SiftingGAN [380] | 2019 | The traditional GAN framework is extended to include an online output method for generating samples, a generative model screening method for model sifting, and a labeled sample discrimination method for sample sifting. | D | |
Neural style transfer | CycleGAN [381] | 2017 | It only needs to build the respective sample sets of the two image style domains, and unpaired samples can be used for training, which greatly reduces the difficulty of building training sample sets and makes the style transfer between any image domains easier to realize. | D |
Pix2Pix [382] | 2017 | Conditional adversarial networks as a general solution to image-to-image translation problems. | D | |
Meta learning data augmentations | Neural augmentation [383] | 2017 | Before the classification network, an augmented network is introduced to input two randomly selected images of the same category, learn the common content information or style information of the two images through the neural network, and then obtain an “enhanced image”, which is input into the classification network together with the original image for classification model training. | D |
Smart Augmentation [384] | 2017 | Reduce network losses by creating a network to learn how to generate enhanced data during the training of the target network. | D | |
Reinforcement learning data augmentation | AutoAugment [385] | 2018 | The search space is designed and has a strategy consisting of many sub-strategies, the best data augmentation is found by an automatic search strategy. | D |
Fast Autoaugment [386] | 2019 | The efficient search strategy based on density matching is used to find better data expansion, thus reducing the time of high order training. | D | |
Faster AutoAugment [387] | 2020 | The differentiable policy search pipeline not only estimates the gradient for many conversion operations with discrete parameters but also provides an efficient mechanism for selecting operations. | D | |
MARL [388] | 2021 | An automatic local patch augmentation method based on multi-agent collaboration and the first to use reinforcement learning to find a patch-level data augmentation strategy. | D | |
RandAugment [389] | 2020 | Simplifies the search space, greatly reduces the computational cost of automatic augmentation, and allows the removal of a separate proxy task. | D |
Reference | Year | Method | Dataset (s) | Imaging Modality | Type of Cancer | Evaluation Metric(s) |
---|---|---|---|---|---|---|
Kavitha et al. [402] | 2021 | BPNN | Mini-MIAS DDSM | Mammography | Breast Cancer | Accuracy: 98.50% Accuracy: 97.55% |
Nawaz et al. [399] | 2018 | DenseNet | BreakHis | histopathological images | Breast Cancer | Accuracy: 95.4% |
Spanhol et al. [403] | 2016 | CNN | BreaKHis | Histopathology | Breast cancer | Accuracy: between 98.87% and 99.34% for the binary classification; between 90.66% and 93.81% for the multi-class classification. |
Fu’adah et al. [391] | 2020 | CNN | ISIC | - | skin cancer | Accuracy: 99% |
Anand et al. [393] | 2022 | DC-ELM | private | histopathological images | Bone Cancer | Accuracy: 97.27% Sensitivity: 98.204% Specificity: 99.568% Precision: 87.832% |
Beevi et al. [394] | 2017 | DBN | MITOS RCC | histopathology images | Breast cancer | F-score: 84.29% F-score: 75% |
Shahweli [395] | 2020 | DBN | IARC TP53 | - | lung cancer | Accuracy: 96% |
Abdel-Zaher et al. [397] | 2016 | DBN | WBCD | - | Breast cancer | Accuracy: 99.68% Sensitivity: 100% Specificity: 99.47% |
Jabeen et al. [400] | 2022 | DarkNet-53 | BUSI | ultrasound images | Breast cancer | Accuracy: 99.1% |
Das et al. [404] | 2019 | DNN | Private dataset | CT/3D | Liver | Accuracy: 99.38% Jaccard index: 98.18% |
Mohsen et al. [405] | 2018 | DNN | Harvard Medical School website | MRI | Brain | Precision: 97% Rate: 96.9% Recall: 97% F-measure: 97% AUC: 98.4% |
El-Ghany et al. [401] | 2023 | ResNet101 | LC25000 | histopathological images | Lung and colon cancer | Precision-99.84% Recall: 99.85% F1-score: 99.84% Specificity: 99.96% Accuracy: 99.94% |
Attallah [406] | 2023 | CerCan·Net | SIPaKMeD and Mendeley | - | Cervical cancer | Accuracy: 97.7% (SIPaKMeD) Accuracy: 100% (Mendeley) |
Reference | Year | Method | Imaging Modality | Type of Cancer | Datasets | Evaluation Metrics |
---|---|---|---|---|---|---|
Shen et al. [420] | 2021 | GMIC | Mammogram | Breast Cancer | NYUBCS + CBIS-DDSM | DSC 1 (Malignant): 0.325 ± 0.231 DSC (Benign): 0.240 ± 0.175 PxAP 2 (Malignant): 0.396 ± 0.275 PxAP (Benign): 0.283 ± 0.244 |
Ranjbarzadeh et al. [421] | 2021 | C-ConvNet/C-CNN | MRI | Brain tumor | BRATS 2018 | Dice (mean): 0.9203 (Whole 3) 0.9113 (Enh 4), 0.8726 (Core 5) Sensitivity (mean): 0.9386 (Whole), 0.9217 (Enh), 0.9712 (Core) HAUSDORFF99 (mm): 1.427 (Whole), 1.669 (Enh), 2.408 (Core) |
Ari et al. [426] | 2018 | ELM-LRF | MRI | Brain Cancer | Simulated datasets | Accuracy: 97.18% Sensitivity: 96.80% Specificity: 97.12% |
Zhang et al. [414] | 2022 | Mask R-CNN | MRI | Breast Cancer | DCE-MRI | Accuracy (mean): 0.86 DSC: 0.82 |
Asuntha et al. [419] | 2020 | FPSOCNN | CT | Lung cancer | WRT 6 | Average accuracy: 94.97% Average sensitivity: 96.68% Average specificity: 95.89% |
LIDC | Average accuracy: 95.62% Average sensitivity: 97.93% Average specificity: 6.32% | |||||
Zhou et al. [418] | 2019 | 3D CNN | MRI | Breast cancer | Private dataset | Accuracy: 83.7% Sensitivity: 90.8% Specificity: 69.3% Overall dice distance: 0.501 ± 0.274 |
Welikala et al. [411] | 2020 | ResNet-101 + Faster R-CNN | MRI | Oral cancer | Private dataset | F1: 87.07% (for identification of images that contained lesions) F1: 78.30% (for the identification of images that required referral) |
Zhang et al. [424] | 2022 | DDTNet | Histopathological image | Breast cancer | BCa-lym | F1: 0.885 Dice: 0.845 PQ 7: 0.731 Time: 0.0674 |
Post-NAT-BRCA | F1: 0.892 Dice: 0.846 PQ: 0.782 Time: 0.0662 | |||||
TCGA-lym | F1: 0.793 Dice: 0.788 PQ: 0.635 Time: 0.0647 | |||||
Maqsood et al. [425] | 2022 | TTCNN | Mammogram | Breast cancer | DDSM + INbreast + MIAS | Accuracy: 97.49% |
Chattopadhyay et al. [427] | 2022 | CNN | MRI | Brain cancer | BRATS | Accuracy: 99.74% |
Luo et al. [422] | 2022 | SCPM-Net | 3D CT | Lung cancer | LUNA16 | Average sensitivity: 89.2% |
Cao et al. [413] | 2019 | Mask R-CNN | Pathological images | Gastric Cancer | Private dataset | AP: 61.2 |
Reference | Year | Method | Datasets | Imaging Modality | Type of Cancer | Evaluation Metrics |
---|---|---|---|---|---|---|
Zhu et al. [439] | 2018 | Adversarial FCN-CRF | Inbreast + DDSM-BCRP | Mammogram | Breast Cancer | Accuracy: 97.0% |
Al-Antari et al. [440] | 2018 | Frcn | Inbreast | Mammogram | Breast Cancer | Dice: 92.69% MCC 1: 85.93% Accuracy: 92.97% JSC 2: 86.37% |
Dong et al. [435] | 2020 | HFCNN | Private dataset | CT | Liver cancer | Dice: 92% |
Shukla et al. [436] | 2022 | Cfcns | 3DIRCAD | CT | Liver cancer | Accuracy: 93.85% |
Ayalew et al. [438] | 2021 | U-Net | 3Dircadb01 + LITS | CT | Liver cancer | Dice: 96% (liver segmentation) Dice: 74% (segmentation of tumors from the liver) Dice: 63% (segmentation of tumor from abdominal CT scan images) |
Li et al. [441] | 2018 | CRU-Net | Inbreast DDSM-BCRP | Mammogram | Breast Cancer | Dice Index: 93.66% (Inbreast) Dice Index: 91.43% (DDSM-BCRP) |
Shen et al. [442] | 2019 | Rescu-Net + MS-rescu-Net | Inbreast | Mammogram | Breast Cancer | Dice: 91.78% Jaccard index: 85.12% Accuracy: 94.16% |
Li et al. [443] | 2019 | U-Net + AGS | DDSM | Mammogram | Breast Cancer | Accuracy: 78.38% Sensitivity: 77.89% F-score: 82.24% |
Hossain [444] | 2019 | U-Net | DDSM | Mammogram | Breast Cancer | Dice: 97.80% F-score: 98.50% Jaccard index: 97.4% |
Sun et al. [445] | 2020 | Aunet | INbreast CBIS-DDSM | Mammogram | Breast Cancer | Dice: 79.10% (INbreast) Dice: 81.80% (CBIS-DDSM) |
Min et al. [446] | 2020 | Mask RCNN | Inbreast | Mammogram | Breast Cancer | Dice: 88.00% |
Al-Antari et al. [447] | 2020 | FrCN | Inbreast | Mammogram | Breast Cancer | Dice: 92.69% Accuracy: 92.97% MCC: 85.93% JAC: 86.37% |
Abdelhafiz et al. [448] | 2020 | Vanilla U-Net | Inbreast + DDSM | Mammogram | Breast Cancer |