{"title":"增强乳腺癌诊断:基于DenseNet的迁移学习与神经散列的组织病理学细粒度图像分类。","authors":"Fatemeh Taheri, Kambiz Rahbar","doi":"10.1007/s11517-025-03346-6","DOIUrl":null,"url":null,"abstract":"<p><p>Breast cancer is one of the most common types of cancer worldwide. The number of breast cancer cases highlights the importance of disease management at various levels. One complementary method for breast cancer classification is microscopic imaging. Manual histopathological image analysis is time-consuming and prone to human errors. Computer-aided diagnosis (CAD) has emerged as a popular and feasible solution for analyzing medical images due to extensive advancements. Microscopic image analysis can assist physicians in more accurate diagnosis. However, the performance of CAD models needs improvement for practical purposes. In the proposed approach, a baseline model called DenseNet is considered for extracting features from histopathological images. The pre-trained DenseNet model alone is not sufficient for fine-grained feature discrimination between benign and malignant histopathological image samples. Therefore, two hash layers are incorporated at the end of the network to enhance feature separability of the two classes, benign and malignant. The performance of the proposed method is evaluated on the BreakHis histopathological image dataset, with magnifications of 40 × , 100 × , 200 × , and 400 × . The evaluation results confirm the effectiveness of the proposed approach compared to other existing approaches. Furthermore, the interpretability of the proposed approach is demonstrated using the LIME technique.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6000,"publicationDate":"2025-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Enhancing breast cancer diagnosis: transfer learning on DenseNet with neural hashing for histopathology fine-grained image classification.\",\"authors\":\"Fatemeh Taheri, Kambiz Rahbar\",\"doi\":\"10.1007/s11517-025-03346-6\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Breast cancer is one of the most common types of cancer worldwide. The number of breast cancer cases highlights the importance of disease management at various levels. One complementary method for breast cancer classification is microscopic imaging. Manual histopathological image analysis is time-consuming and prone to human errors. Computer-aided diagnosis (CAD) has emerged as a popular and feasible solution for analyzing medical images due to extensive advancements. Microscopic image analysis can assist physicians in more accurate diagnosis. However, the performance of CAD models needs improvement for practical purposes. In the proposed approach, a baseline model called DenseNet is considered for extracting features from histopathological images. The pre-trained DenseNet model alone is not sufficient for fine-grained feature discrimination between benign and malignant histopathological image samples. Therefore, two hash layers are incorporated at the end of the network to enhance feature separability of the two classes, benign and malignant. The performance of the proposed method is evaluated on the BreakHis histopathological image dataset, with magnifications of 40 × , 100 × , 200 × , and 400 × . The evaluation results confirm the effectiveness of the proposed approach compared to other existing approaches. Furthermore, the interpretability of the proposed approach is demonstrated using the LIME technique.</p>\",\"PeriodicalId\":49840,\"journal\":{\"name\":\"Medical & Biological Engineering & Computing\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":2.6000,\"publicationDate\":\"2025-04-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Medical & Biological Engineering & Computing\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1007/s11517-025-03346-6\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Medical & Biological Engineering & Computing","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1007/s11517-025-03346-6","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
Enhancing breast cancer diagnosis: transfer learning on DenseNet with neural hashing for histopathology fine-grained image classification.
Breast cancer is one of the most common types of cancer worldwide. The number of breast cancer cases highlights the importance of disease management at various levels. One complementary method for breast cancer classification is microscopic imaging. Manual histopathological image analysis is time-consuming and prone to human errors. Computer-aided diagnosis (CAD) has emerged as a popular and feasible solution for analyzing medical images due to extensive advancements. Microscopic image analysis can assist physicians in more accurate diagnosis. However, the performance of CAD models needs improvement for practical purposes. In the proposed approach, a baseline model called DenseNet is considered for extracting features from histopathological images. The pre-trained DenseNet model alone is not sufficient for fine-grained feature discrimination between benign and malignant histopathological image samples. Therefore, two hash layers are incorporated at the end of the network to enhance feature separability of the two classes, benign and malignant. The performance of the proposed method is evaluated on the BreakHis histopathological image dataset, with magnifications of 40 × , 100 × , 200 × , and 400 × . The evaluation results confirm the effectiveness of the proposed approach compared to other existing approaches. Furthermore, the interpretability of the proposed approach is demonstrated using the LIME technique.
期刊介绍:
Founded in 1963, Medical & Biological Engineering & Computing (MBEC) continues to serve the biomedical engineering community, covering the entire spectrum of biomedical and clinical engineering. The journal presents exciting and vital experimental and theoretical developments in biomedical science and technology, and reports on advances in computer-based methodologies in these multidisciplinary subjects. The journal also incorporates new and evolving technologies including cellular engineering and molecular imaging.
MBEC publishes original research articles as well as reviews and technical notes. Its Rapid Communications category focuses on material of immediate value to the readership, while the Controversies section provides a forum to exchange views on selected issues, stimulating a vigorous and informed debate in this exciting and high profile field.
MBEC is an official journal of the International Federation of Medical and Biological Engineering (IFMBE).