Contextual Features and Information Bottleneck-Based Multi-Input Network for Breast Cancer Classification from Contrast-Enhanced Spectral Mammography
Abstract
:1. Introduction
2. Related Work
3. Methods and Materials
3.1. The Proposed CESM Classification Method
3.1.1. Feature Extraction Module
3.1.2. Feature Selection Module
3.2. Materials
3.2.1. Data and Preprocessing
3.2.2. Details of Training
4. Results
4.1. Qualitative Comparison
4.2. Quantitative Comparison
4.3. Ablation Studies
5. Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Method | Dataset | Accuracy | AUC | ||
---|---|---|---|---|---|
Type | Source | Number | |||
Multilayer Perceptron Classifier (Danala et al., 2018) [28] | LE & DES | Clinical Database of Mayo Clinic Arizona | 111 | - | 0.848 |
SD-CNN (Gao et al., 2018) [31] | LE & DES | Mayo Clinic Arizona & INbreast | 49 & 89 | 0.900 | 0.920 |
Support Vector Machine (Losurdo et al., 2019) [27] | CC-DES & MLO-DES | Istituto Tumori “Giovanni Paolo II” | 55 | 0.800 | - |
Random Forest Classifier (Fanizzi et al., 2019) [32] | CC-DES & MLO-DES | Istituto Tumori “Giovanni Paolo II” | 58 | 0.825 | 0.850 |
Fine-tuning Pretrained AlexNet (Perek et al., 2019) [33] | CC-DES & MLO-DES & text | - | 129 | 0.880 | 0.897 |
Radiomics Analysis (Marino et al., 2020) [26] | DES | Tertiary Referral Academic Center | 100 | - | - |
Fine-tuning CheXNet (Dominique et al., 2022) [34] | LE & DES | Henri Becquerel Cancer Center | 447 | 0.874 | 0.910 |
RefineNet with XGBoost Classifier (Zhang et al., 2022) [35] | CC-LE & CC-DES | Yantai Yuhuangding Hospital and Fudan University Cancer Center | 1355 | 0.802 | 0.867 |
Method | Accuracy | Precision | Sensitivity | Specificity | F1-Score |
---|---|---|---|---|---|
VGG-16 | 0.8348 | 0.8354 | 0.8411 | 0.8364 | 0.8376 |
VGG-19 | 0.8511 | 0.8476 | 0.8561 | 0.8461 | 0.8510 |
ResNet-18 | 0.8467 | 0.8412 | 0.8547 | 0.8387 | 0.8479 |
ResNet-50 | 0.8572 | 0.8474 | 0.8693 | 0.8431 | 0.8592 |
Ours | 0.8806 | 0.8803 | 0.8810 | 0.8801 | 0.8806 |
Method | Accuracy | Precision | Sensitivity | Specificity | F1-Score |
---|---|---|---|---|---|
ResNet-50 | 0.8572 | 0.8474 | 0.8693 | 0.8431 | 0.8592 |
ResNet-50&CA | 0.8617 | 0.8550 | 0.8594 | 0.8542 | 0.8602 |
ResNet-50&IB | 0.8609 | 0.8597 | 0.8814 | 0.8553 | 0.8689 |
Ours (full) | 0.8806 | 0.8803 | 0.8810 | 0.8801 | 0.8806 |
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Li, X.; Cui, J.; Song, J.; Jia, M.; Zou, Z.; Ding, G.; Zheng, Y. Contextual Features and Information Bottleneck-Based Multi-Input Network for Breast Cancer Classification from Contrast-Enhanced Spectral Mammography. Diagnostics 2022, 12, 3133. https://doi.org/10.3390/diagnostics12123133
Li X, Cui J, Song J, Jia M, Zou Z, Ding G, Zheng Y. Contextual Features and Information Bottleneck-Based Multi-Input Network for Breast Cancer Classification from Contrast-Enhanced Spectral Mammography. Diagnostics. 2022; 12(12):3133. https://doi.org/10.3390/diagnostics12123133
Chicago/Turabian StyleLi, Xinmeng, Jia Cui, Jingqi Song, Mingyu Jia, Zhenxing Zou, Guocheng Ding, and Yuanjie Zheng. 2022. "Contextual Features and Information Bottleneck-Based Multi-Input Network for Breast Cancer Classification from Contrast-Enhanced Spectral Mammography" Diagnostics 12, no. 12: 3133. https://doi.org/10.3390/diagnostics12123133
APA StyleLi, X., Cui, J., Song, J., Jia, M., Zou, Z., Ding, G., & Zheng, Y. (2022). Contextual Features and Information Bottleneck-Based Multi-Input Network for Breast Cancer Classification from Contrast-Enhanced Spectral Mammography. Diagnostics, 12(12), 3133. https://doi.org/10.3390/diagnostics12123133