Diagnosis of Breast Cancer with Strongly Supervised Deep Learning Neural Network
Abstract
:1. Introduction
2. Materials and Methods
2.1. Materials
2.2. Methods
2.2.1. Slice-Level ROI
2.2.2. Slice-Level Training and Classification
2.2.3. Lesion-Level Diagnosis
3. Results
3.1. Performance Metrics
- True positive (TP): predicted positive in positive samples (malignant lesions).
- True negative (TN): predicted negative in negative samples (benign lesions).
- False positive (FP): predicted positive in negative samples.
- False negative (FN): predicted negative in positive samples.
3.2. Slice-Level ROI
3.3. Lesion-Level Diagnosis
3.4. DCNNs Combined with Slice-Level ROI and Lesion-Level Diagnosis
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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References | Dataset | Classification Method Used | ROI | Features | Diagnosis Metrics | Limitations |
---|---|---|---|---|---|---|
[12] | Ultrasound images: 184 benign 264 malignant | SVM | Whole Ultrasound | Radiomics features | Lesion-level (one slice) | Professional instruments required Features chosen with subjective bias Discarded the other slices |
[13] | DCE-MRI: 42 benign 78 malignant | SVM | Whole DCE-MRI | Radiomics features | Lesion-level (one slice) | Small dataset Professional instruments required Features chosen with subjective bias Discarded the other slices |
[14] | DCE-MRI: 40 benign 73 malignant | SVM | Whole DCE-MRI | Radiomics features | Lesion-level (one slice) | Small dataset Professional instruments required Discarded the other slices |
[15] | DCE-MRI: 85 benign 149 malignant | SVM | Whole DCE-MRI | DWI Features + Radiomics features | Lesion-level (one slice) | Small dataset Professional instruments required Discarded the other slices |
[16] | DCE-MRI: 149 benign 368 malignant | Random Forest | Whole DCE-MRI | Radiomics features + Feature maps extracted by DCNN | Lesion-level (one slice) | Rough ROI affect the performance (the highest value was 0.85) Discarded the other slices |
[17] | DCE-MRI: 75 lesions | Unet++ and Faster RCNN | Segmented breast | Feature maps extracted by DCNN | Lesion-level (one slice) | Small dataset Without diagnosis |
[18] | DCE-MRI: 438 patients, 3167 slices | ResNet101 | Whole DCE-MRI | Feature maps extracted by DCNN | Slice-level | Rough ROI DCNN training and testing with slice-level data, can not stand for the performance in lesion-level diagnosis |
[19] | DCE-MRI: 506 benign 1031 malignant | 3D DenseNet | Segmented breast | Feature maps extracted by DCNN | 3D lesion-level (one slice) | Rough ROI Discarded the other slices |
[20] | DCE-MRI: 88 benign 139 malignant | ResNet50/Random Forest | Lesion level smallest bounding box | Feature maps extracted by DCNN | 3D lesion-level (slice with max score) | Lesion-level ROI Lesion-level diagnosis with the max score slice (Discarded the other slices) |
Dataset | Training | Validation | Testing | Subtotal |
---|---|---|---|---|
Benign lesions | 162 | 45 | 60 | 267 |
Malign lesions | 115 | 45 | 60 | 220 |
Benign slices | 1278 | 259 | 329 | 1866 |
Malign slices | 1053 | 341 | 453 | 1847 |
Lesion Type | Lesion Name | Number |
---|---|---|
Malignant | ||
Invasive cancer | 142 | |
Ductal carcinoma in situ | 53 | |
Semi-invasive carcinoma in situ | 8 | |
Premium Executive Internal Cancer | 5 | |
Invasive lobular carcinoma | 5 | |
low-grade intraductal carcinoma | 1 | |
Intermediate-grade intraductal carcinoma | 1 | |
Intraductal papillary carcinoma | 1 | |
Invasive intraductal carcinoma | 1 | |
Squamous cell carcinoma | 1 | |
Eczematous breast cancer | 1 | |
Calcified intraductal carcinoma | 1 | |
Benign | ||
Fibroadenoma | 160 | |
Fibrocystic breast disease | 39 | |
Intraductal papilloma | 33 | |
Adenopathy | 7 | |
Papillary hyperplasia | 4 | |
Hyperplastic nodule | 3 | |
Lymphoma infiltration | 3 | |
Lymphoma | 3 | |
Chronic inflammation | 2 | |
Fibroadenosis with lymphoid tissue | 2 | |
Chronic granulomatous inflammation | 1 | |
Complex sclerosing lesions | 1 | |
Glandular hyperplasia with myoepithelial hyperplasia, | 1 | |
Interstitial fibrous collagen hyperplasia | 1 | |
Myoepithelial tumor | 1 | |
Calcified nodules | 1 | |
Dermal fibroma | 1 | |
Compliant with BI-RADS Class II | 1 | |
Compliant with BI-RADS Class III | 1 | |
Fibroadenoma with tubular adenoma | 1 | |
Epithelial hyperplasia | 1 |
DCNN | ROI Type | Threshold | Sensitivity | Specificity | Accuracy | AUC |
---|---|---|---|---|---|---|
DenseNet | SBBS | 0.520 | 0.950 | 0.900 | 0.925 | 0.958 |
SBBL | 0.500 | 0.700 | 0.767 | 0.733 | 0.742 | |
SL | 0.500 | 0.800 | 0.867 | 0.833 | 0.909 | |
VGG16 | SBBS | 0.480 | 0.917 | 0.867 | 0.892 | 0.961 |
SBBL | 0.510 | 0.717 | 0.783 | 0.750 | 0.804 | |
SL | 0.470 | 0.867 | 0.767 | 0.817 | 0.907 | |
ResNet50 | SBBS | 0.460 | 0.950 | 0.850 | 0.900 | 0.934 |
SBBL | 0.520 | 0.700 | 0.867 | 0.783 | 0.800 | |
SL | 0.490 | 0.767 | 0.900 | 0.833 | 0.883 | |
GoogLeNet | SBBS | 0.490 | 0.967 | 0.817 | 0.892 | 0.952 |
SBBL | 0.450 | 0.883 | 0.767 | 0.825 | 0.883 | |
SL | 0.500 | 0.867 | 0.883 | 0.850 | 0.914 | |
AlexNet | SBBS | 0.470 | 0.917 | 0.900 | 0.909 | 0.948 |
SBBL | 0.530 | 0.700 | 0.883 | 0.792 | 0.837 | |
SL | 0.550 | 0.633 | 0.867 | 0.750 | 0.797 | |
Average | SBBS | 0.484 | 0.940 | 0.867 | 0.903 | 0.951 |
SBBL | 0.502 | 0.740 | 0.813 | 0.777 | 0.813 | |
SL | 0.502 | 0.787 | 0.857 | 0.817 | 0.882 |
DCNN | Weighting Methods | Threshold | Sensitivity | Specificity | Accuracy | AUC |
---|---|---|---|---|---|---|
ResNet50 | Average | 0.460 | 0.950 | 0.850 | 0.900 | 0.934 |
Highest score | 0.440 | 0.800 | 0.900 | 0.850 | 0.887 | |
Area ratio | 0.440 | 0.917 | 0.833 | 0.875 | 0.919 | |
Perimeter ratio | 0.440 | 0.917 | 0.833 | 0.875 | 0.918 | |
DenseNet | Average | 0.520 | 0.950 | 0.900 | 0.925 | 0.958 |
Highest score | 0.530 | 0.967 | 0.900 | 0.933 | 0.955 | |
Area ratio | 0.520 | 0.950 | 0.917 | 0.933 | 0.939 | |
Perimeter ratio | 0.520 | 0.950 | 0.900 | 0.925 | 0.940 | |
VGG16 | Average | 0.480 | 0.917 | 0.867 | 0.892 | 0.961 |
Highest score | 0.570 | 0.830 | 0.950 | 0.892 | 0.949 | |
Area ratio | 0.480 | 0.900 | 0.867 | 0.883 | 0.945 | |
Perimeter ratio | 0.480 | 0.900 | 0.867 | 0.883 | 0.947 | |
GoogLeNet | Average | 0.490 | 0.967 | 0.817 | 0.892 | 0.952 |
Highest score | 0.600 | 0.900 | 0.883 | 0.892 | 0.959 | |
Area ratio | 0.490 | 0.933 | 0.800 | 0.867 | 0.937 | |
Perimeter ratio | 0.490 | 0.967 | 0.780 | 0.875 | 0.937 | |
AlexNet | Average | 0.470 | 0.917 | 0.900 | 0.909 | 0.948 |
Highest score | 0.480 | 0.850 | 0.917 | 0.883 | 0.904 | |
Area ratio | 0.470 | 0.883 | 0.867 | 0.875 | 0.934 | |
Perimeter ratio | 0.470 | 0.883 | 0.883 | 0.883 | 0.929 | |
Average Score | Average | 0.484 | 0.940 | 0.867 | 0.903 | 0.951 |
Highest score | 0.524 | 0.869 | 0.910 | 0.890 | 0.930 | |
Area ratio | 0.480 | 0.917 | 0.857 | 0.887 | 0.935 | |
Perimeter ratio | 0.480 | 0.923 | 0.853 | 0.888 | 0.934 |
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Gui, H.; Su, T.; Pang, Z.; Jiao, H.; Xiong, L.; Jiang, X.; Li, L.; Wang, Z. Diagnosis of Breast Cancer with Strongly Supervised Deep Learning Neural Network. Electronics 2022, 11, 3003. https://doi.org/10.3390/electronics11193003
Gui H, Su T, Pang Z, Jiao H, Xiong L, Jiang X, Li L, Wang Z. Diagnosis of Breast Cancer with Strongly Supervised Deep Learning Neural Network. Electronics. 2022; 11(19):3003. https://doi.org/10.3390/electronics11193003
Chicago/Turabian StyleGui, Haitian, Tao Su, Zhiyong Pang, Han Jiao, Lang Xiong, Xinhua Jiang, Li Li, and Zixin Wang. 2022. "Diagnosis of Breast Cancer with Strongly Supervised Deep Learning Neural Network" Electronics 11, no. 19: 3003. https://doi.org/10.3390/electronics11193003
APA StyleGui, H., Su, T., Pang, Z., Jiao, H., Xiong, L., Jiang, X., Li, L., & Wang, Z. (2022). Diagnosis of Breast Cancer with Strongly Supervised Deep Learning Neural Network. Electronics, 11(19), 3003. https://doi.org/10.3390/electronics11193003