Classifying breast intraductal proliferative lesions via a knowledge distillation framework using convolutional neural network-based nuclei-segmentation-assisted classification (KDCNN-NSAC)
Background and objective: Diagnosis of breast intraductal proliferative lesions (BIDPLs) in hematoxylin-eosin (HE) images remains a time-consuming and intractable topic because of subjective processes and subtle morphological differences. Convolutional neural networks (CNNs) show great potential for...
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Elsevier
2025-05-01
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844025011375 |
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| author | Xiangmin Li Jiamei Chen Bo Luo Minyan Xia Xu Zhang Hangjia Zhu Yutian Zhang-Cai Yongshun Chen Yang Yang Yaofeng Wen |
| author_facet | Xiangmin Li Jiamei Chen Bo Luo Minyan Xia Xu Zhang Hangjia Zhu Yutian Zhang-Cai Yongshun Chen Yang Yang Yaofeng Wen |
| author_sort | Xiangmin Li |
| collection | DOAJ |
| description | Background and objective: Diagnosis of breast intraductal proliferative lesions (BIDPLs) in hematoxylin-eosin (HE) images remains a time-consuming and intractable topic because of subjective processes and subtle morphological differences. Convolutional neural networks (CNNs) show great potential for providing objective analysis strategies for HE images. In this study, we proposed a novel knowledge distillation (KD) framework using CNN-based nuclei segmentation-assisted classification (KDCNN-NSAC). Methods: The diagnosis of BIDPLs is treated as multiple class classification tasks in the BReAst Carcinoma Subtyping dataset. The KDCNN-NSAC fully leveraged the epithelial and stromal nuclei-level features in training phases and performed region-of-interest (ROI)-level classifications in predicting phases. Then, the whole slide image (WSI) was diagnosed based on the risk ratings of the ROIs within it, instead of processing a WSI. Results: The principal results showed that in ROI-level classifications, KDCNN-NSAC outperformed the state-of-the-art methods for 7-class classification with an average F1 score of 63.26 % and achieves F1 score of 98.36 % and 94.21 %, respectively, in distinguishing BIDPLs from invasive cancer and normal tissue. The WSI-level predictions obtained a high degree of consistency with the pathologists’ annotation (kappa value of 0.88). Ablation experiments showed that nuclei segmentation and classification components improve the performance of the baseline model in KDCNN-NSAC by 3 %. Conclusions: The KDCNN-NSAC makes the model focus on important cellular information and predicts the WSI in accordance with the pathologists’ diagnostic thinking, thus improving model explainability. Moreover, the introduce of KDCNN-NSAC will help achieve superior performance in diagnosing BIDPLs. |
| format | Article |
| id | doaj-art-e4ba64ebec3f4c0ab9ab2cd214e2fb40 |
| institution | DOAJ |
| issn | 2405-8440 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj-art-e4ba64ebec3f4c0ab9ab2cd214e2fb402025-08-20T03:19:56ZengElsevierHeliyon2405-84402025-05-011110e4275610.1016/j.heliyon.2025.e42756Classifying breast intraductal proliferative lesions via a knowledge distillation framework using convolutional neural network-based nuclei-segmentation-assisted classification (KDCNN-NSAC)Xiangmin Li0Jiamei Chen1Bo Luo2Minyan Xia3Xu Zhang4Hangjia Zhu5Yutian Zhang-Cai6Yongshun Chen7Yang Yang8Yaofeng Wen9School of Mechanical Engineering, Shanghai Dianji University, Shanghai, 201306, ChinaCenter of Oncology, Renmin Hospital of Wuhan University, Wuhan, 430060, ChinaDepartment of Pathology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430014, ChinaSchool of Mechanical Engineering, Shanghai Dianji University, Shanghai, 201306, ChinaSchool of Mechanical Engineering, Shanghai Dianji University, Shanghai, 201306, ChinaCenter of Oncology, Renmin Hospital of Wuhan University, Wuhan, 430060, ChinaCenter of Oncology, Renmin Hospital of Wuhan University, Wuhan, 430060, ChinaCenter of Oncology, Renmin Hospital of Wuhan University, Wuhan, 430060, China; Corresponding author.School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, China; Shanghai Lanhui Medical Technology Co, Shanghai, 200063, China; Corresponding author. School of Biomedical Engineering, Shanghai Jiao Tong University Shanghai Lanhui Medical Technology Co, Shanghai, 200063, China.School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, China; Shanghai Lanhui Medical Technology Co, Shanghai, 200063, China; Corresponding author. School of Biomedical Engineering, Shanghai Jiao Tong University Shanghai Lanhui Medical Technology Co, Shanghai, 200063, China.Background and objective: Diagnosis of breast intraductal proliferative lesions (BIDPLs) in hematoxylin-eosin (HE) images remains a time-consuming and intractable topic because of subjective processes and subtle morphological differences. Convolutional neural networks (CNNs) show great potential for providing objective analysis strategies for HE images. In this study, we proposed a novel knowledge distillation (KD) framework using CNN-based nuclei segmentation-assisted classification (KDCNN-NSAC). Methods: The diagnosis of BIDPLs is treated as multiple class classification tasks in the BReAst Carcinoma Subtyping dataset. The KDCNN-NSAC fully leveraged the epithelial and stromal nuclei-level features in training phases and performed region-of-interest (ROI)-level classifications in predicting phases. Then, the whole slide image (WSI) was diagnosed based on the risk ratings of the ROIs within it, instead of processing a WSI. Results: The principal results showed that in ROI-level classifications, KDCNN-NSAC outperformed the state-of-the-art methods for 7-class classification with an average F1 score of 63.26 % and achieves F1 score of 98.36 % and 94.21 %, respectively, in distinguishing BIDPLs from invasive cancer and normal tissue. The WSI-level predictions obtained a high degree of consistency with the pathologists’ annotation (kappa value of 0.88). Ablation experiments showed that nuclei segmentation and classification components improve the performance of the baseline model in KDCNN-NSAC by 3 %. Conclusions: The KDCNN-NSAC makes the model focus on important cellular information and predicts the WSI in accordance with the pathologists’ diagnostic thinking, thus improving model explainability. Moreover, the introduce of KDCNN-NSAC will help achieve superior performance in diagnosing BIDPLs.http://www.sciencedirect.com/science/article/pii/S2405844025011375Breast intraductal proliferative lesionsBreast cancerKnowledge distillationConvolutional neural networkNuclei segmentationClassification |
| spellingShingle | Xiangmin Li Jiamei Chen Bo Luo Minyan Xia Xu Zhang Hangjia Zhu Yutian Zhang-Cai Yongshun Chen Yang Yang Yaofeng Wen Classifying breast intraductal proliferative lesions via a knowledge distillation framework using convolutional neural network-based nuclei-segmentation-assisted classification (KDCNN-NSAC) Heliyon Breast intraductal proliferative lesions Breast cancer Knowledge distillation Convolutional neural network Nuclei segmentation Classification |
| title | Classifying breast intraductal proliferative lesions via a knowledge distillation framework using convolutional neural network-based nuclei-segmentation-assisted classification (KDCNN-NSAC) |
| title_full | Classifying breast intraductal proliferative lesions via a knowledge distillation framework using convolutional neural network-based nuclei-segmentation-assisted classification (KDCNN-NSAC) |
| title_fullStr | Classifying breast intraductal proliferative lesions via a knowledge distillation framework using convolutional neural network-based nuclei-segmentation-assisted classification (KDCNN-NSAC) |
| title_full_unstemmed | Classifying breast intraductal proliferative lesions via a knowledge distillation framework using convolutional neural network-based nuclei-segmentation-assisted classification (KDCNN-NSAC) |
| title_short | Classifying breast intraductal proliferative lesions via a knowledge distillation framework using convolutional neural network-based nuclei-segmentation-assisted classification (KDCNN-NSAC) |
| title_sort | classifying breast intraductal proliferative lesions via a knowledge distillation framework using convolutional neural network based nuclei segmentation assisted classification kdcnn nsac |
| topic | Breast intraductal proliferative lesions Breast cancer Knowledge distillation Convolutional neural network Nuclei segmentation Classification |
| url | http://www.sciencedirect.com/science/article/pii/S2405844025011375 |
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