Special Issue "Interpretable Deep Learning in Electronics, Computer Science and Medical Imaging"

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: closed (31 August 2020).

Special Issue Editor

Prof. Dr. Yoichi Hayashi
Website1 Website2
Guest Editor
Artificial Intelligence Lab, Department of Computer Science, Meiji University, Kawasaki, Kanagawa 214-8571, Japan
Interests: artificial intelligence; deep learning; classification; rule extraction; big data analytics; interpretability of deep neural network
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Special Issue Information

Dear Colleagues,

Artificial intelligence (AI), particularly, deep learning (DL), which involves automated feature extraction using deep neural networks (DNNs), has been used increasingly by electronics engineer, computer scientist, and physicians. AI can analyze computer vision and medical images at a level not possible by a single person. However, the resulting parameters are difficult to interpret. This so-called “black box” problem causes opaqueness in DL.

The aim of the Special Issue is to help realize interpretable DL in electronics, computer science, and medical imaging. To achieve this aim, we should attempt to bring about a paradigm shift in electronics, computer science, and medical imaging in which diagnostic accuracy is surpassed to achieve explainability. DL in medical imaging has still considerable limitations. To interpret and apply DL to medical imaging tasks effectively, sufficient expertise in computer science is required. We should interpret elements of decision-making behind classification decisions. While DL algorithms can markedly enhance the quantitative performance, such as accuracy, interpretability is a vital component.

Moreover, establishing accountability is one of the most important issues in medical imaging to explain the classification results clearly. Although more interpretable algorithms seem likely to be more readily accepted by electronics, computers science, and medical professionals, it remains necessary to determine whether this could increase effectiveness in electronics, computer science, and medical imaging. For the acceptance of AI by electronics engineers, computer scientists, and physicians, not only quantitative, but also qualitative algorithmic performance should be improved.

Topics of interest of this Special Issue include, but are not limited to:

  • Interpretable DL in electronics
  • Interpretable DL in computer science
  • Interpretable DL in medical imaging
  • Non-black-box machine learning
  • Interpretable large decision trees and random forests
  • Interpretable machine learning
  • Converting deep neural network to decision trees
  • Interpretable decision trees

Prof. Dr. Yoichi Hayashi
Guest Editor

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Published Papers (6 papers)

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Research

Open AccessCommunication
Does Deep Learning Work Well for Categorical Datasets with Mainly Nominal Attributes?
Electronics 2020, 9(11), 1966; https://doi.org/10.3390/electronics9111966 - 21 Nov 2020
Abstract
Given the complexity of real-world datasets, it is difficult to present data structures using existing deep learning (DL) models. Most research to date has concentrated on datasets with only one type of attribute: categorical or numerical. Categorical data are common in datasets such [...] Read more.
Given the complexity of real-world datasets, it is difficult to present data structures using existing deep learning (DL) models. Most research to date has concentrated on datasets with only one type of attribute: categorical or numerical. Categorical data are common in datasets such as the German (-categorical) credit scoring dataset, which contains numerical, ordinal, and nominal attributes. The heterogeneous structure of this dataset makes very high accuracy difficult to achieve. DL-based methods have achieved high accuracy (99.68%) for the Wisconsin Breast Cancer Dataset, whereas DL-inspired methods have achieved high accuracy (97.39%) for the Australian credit dataset. However, to our knowledge, no such method has been proposed to classify the German credit dataset. This study aimed to provide new insights into the reasons why DL-based and DL-inspired classifiers do not work well for categorical datasets, mainly consisting of nominal attributes. We also discuss the problems associated with using nominal attributes to design high-performance classifiers. Considering the expanded utility of DL, this study's findings should aid in the development of a new type of DL that can handle categorical datasets consisting of mainly nominal attributes, which are commonly used in risk evaluation, finance, banking, and marketing. Full article
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Open AccessArticle
Guided Networks for Few-Shot Image Segmentation and Fully Connected CRFs
Electronics 2020, 9(9), 1508; https://doi.org/10.3390/electronics9091508 - 14 Sep 2020
Abstract
The goal of the few-shot learning method is to learn quickly from a low-data regime. Structured output tasks like segmentation are challenging for few-shot learning, due to their being high-dimensional and statistically dependent. For this problem, we propose improved guided networks and combine [...] Read more.
The goal of the few-shot learning method is to learn quickly from a low-data regime. Structured output tasks like segmentation are challenging for few-shot learning, due to their being high-dimensional and statistically dependent. For this problem, we propose improved guided networks and combine them with a fully connected conditional random field (CRF). The guided network extracts task representations from annotated support images through feature fusion to do fast, accurate inference on new unannotated query images. By bringing together few-shot learning methods and fully connected CRFs, our method can do accurate object segmentation by overcoming poor localization properties of deep convolutional neural networks and can quickly updating tasks, without further optimization, when faced with new data. Our guided network is at the forefront of accuracy for the terms of annotation volume and time. Full article
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Open AccessFeature PaperArticle
One-Dimensional Convolutional Neural Networks with Feature Selection for Highly Concise Rule Extraction from Credit Scoring Datasets with Heterogeneous Attributes
Electronics 2020, 9(8), 1318; https://doi.org/10.3390/electronics9081318 - 16 Aug 2020
Cited by 1
Abstract
Convolution neural networks (CNNs) have proven effectiveness, but they are not applicable to all datasets, such as those with heterogeneous attributes, which are often used in the finance and banking industries. Such datasets are difficult to classify, and to date, existing high-accuracy classifiers [...] Read more.
Convolution neural networks (CNNs) have proven effectiveness, but they are not applicable to all datasets, such as those with heterogeneous attributes, which are often used in the finance and banking industries. Such datasets are difficult to classify, and to date, existing high-accuracy classifiers and rule-extraction methods have not been able to achieve sufficiently high classification accuracies or concise classification rules. This study aims to provide a new approach for achieving transparency and conciseness in credit scoring datasets with heterogeneous attributes by using a one-dimensional (1D) fully-connected layer first CNN combined with the Recursive-Rule Extraction (Re-RX) algorithm with a J48graft decision tree (hereafter 1D FCLF-CNN). Based on a comparison between the proposed 1D FCLF-CNN and existing rule extraction methods, our architecture enabled the extraction of the most concise rules (6.2) and achieved the best accuracy (73.10%), i.e., the highest interpretability–priority rule extraction. These results suggest that the 1D FCLF-CNN with Re-RX with J48graft is very effective for extracting highly concise rules for heterogeneous credit scoring datasets. Although it does not completely overcome the accuracy–interpretability dilemma for deep learning, it does appear to resolve this issue for credit scoring datasets with heterogeneous attributes, and thus, could lead to a new era in the financial industry. Full article
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Open AccessFeature PaperArticle
A Two-Step Rule-Extraction Technique for a CNN
Electronics 2020, 9(6), 990; https://doi.org/10.3390/electronics9060990 - 13 Jun 2020
Abstract
The explanation of the decisions provided by a model are crucial in a domain such as medical diagnosis. With the advent of deep learning, it is very important to explain why a classification is reached by a model. This work tackles the transparency [...] Read more.
The explanation of the decisions provided by a model are crucial in a domain such as medical diagnosis. With the advent of deep learning, it is very important to explain why a classification is reached by a model. This work tackles the transparency problem of convolutional neural networks(CNNs). We propose to generate propositional rules from CNNs, because they are intuitive to the way humans reason. Our method considers that a CNN is the union of two subnetworks: a multi-layer erceptron (MLP) in the fully connected layers; and a subnetwork including several 2D convolutional layers and max-pooling layers. Rule extraction exhibits two main steps, with each step generating rules from each subnetwork of the CNN. In practice, we approximate the two subnetworks by two particular MLP models that makes it possible to generate propositional rules. We performed the experiments with two datasets involving images: MNISTdigit recognition; and skin-cancer diagnosis. With high fidelity, the extracted rules designated the location of discriminant pixels, as well as the conditions that had to be met to achieve the classification. We illustrated several examples of rules by their centroids and their discriminant pixels. Full article
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Open AccessArticle
Channel and Spatial Attention Regression Network for Cup-to-Disc Ratio Estimation
Electronics 2020, 9(6), 909; https://doi.org/10.3390/electronics9060909 - 29 May 2020
Abstract
Cup-to-disc ratio (CDR) is of great importance during assessing structural changes at the optic nerve head (ONH) and diagnosis of glaucoma. While most efforts have been put on acquiring the CDR number through CNN-based segmentation algorithms followed by the calculation of CDR, these [...] Read more.
Cup-to-disc ratio (CDR) is of great importance during assessing structural changes at the optic nerve head (ONH) and diagnosis of glaucoma. While most efforts have been put on acquiring the CDR number through CNN-based segmentation algorithms followed by the calculation of CDR, these methods usually only focus on the features in the convolution kernel, which is, after all, the operation of the local region, ignoring the contribution of rich global features (such as distant pixels) to the current features. In this paper, a new end-to-end channel and spatial attention regression deep learning network is proposed to deduces CDR number from the regression perspective and combine the self-attention mechanism with the regression network. Our network consists of four modules: the feature extraction module to extract deep features expressing the complicated pattern of optic disc (OD) and optic cup (OC), the attention module including the channel attention block (CAB) and the spatial attention block (SAB) to improve feature representation by aggregating long-range contextual information, the regression module to deduce CDR number directly, and the segmentation-auxiliary module to focus the model’s attention on the relevant features instead of the background region. Especially, the CAB selects relatively important feature maps in channel dimension, shifting the emphasis on the OD and OC region; meanwhile, the SAB learns the discriminative ability of feature representation at pixel level by capturing the relationship of intra-feature map. The experimental results of ORIGA dataset show that our method obtains absolute CDR error of 0.067 and the Pearson’s correlation coefficient of 0.694 in estimating CDR and our method has a great potential in predicting the CDR number. Full article
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Open AccessArticle
On the Interpretability of Machine Learning Models and Experimental Feature Selection in Case of Multicollinear Data
Electronics 2020, 9(5), 761; https://doi.org/10.3390/electronics9050761 - 06 May 2020
Abstract
In the field of machine learning, a considerable amount of research is involved in the interpretability of models and their decisions. The interpretability contradicts the model quality. Random Forests are among the best quality technologies of machine learning, but their operation is of [...] Read more.
In the field of machine learning, a considerable amount of research is involved in the interpretability of models and their decisions. The interpretability contradicts the model quality. Random Forests are among the best quality technologies of machine learning, but their operation is of “black box” character. Among the quantifiable approaches to the model interpretation, there are measures of association of predictors and response. In case of the Random Forests, this approach usually consists of calculating the model’s feature importances. Known methods, including the built-in one, are less suitable in settings with strong multicollinearity of features. Therefore, we propose an experimental approach to the feature selection task, a greedy forward feature selection method with least-trees-used criterion. It yields a set of most informative features that can be used in a machine learning (ML) training process with similar prediction quality as the original feature set. We verify the results of the proposed method on two known datasets, one with small feature multicollinearity and another with large feature multicollinearity. The proposed method also allows for a domain expert help with selecting among equally important features, which is known as the human-in-the-loop approach. Full article
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