{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,13]],"date-time":"2026-09-13T02:43:09Z","timestamp":1789267389685,"version":"build-2803163510"},"reference-count":44,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2022,6,20]],"date-time":"2022-06-20T00:00:00Z","timestamp":1655683200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61905182"],"award-info":[{"award-number":["61905182"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62075200"],"award-info":[{"award-number":["62075200"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82002979"],"award-info":[{"award-number":["82002979"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2021CFA042"],"award-info":[{"award-number":["2021CFA042"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2020020601012237"],"award-info":[{"award-number":["2020020601012237"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2020BAB005"],"award-info":[{"award-number":["2020BAB005"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["lcyf202010"],"award-info":[{"award-number":["lcyf202010"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["ZNXKPY2022017"],"award-info":[{"award-number":["ZNXKPY2022017"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100019537","name":"Science Fund for Distinguished Young Scholars of Hubei Provinc","doi-asserted-by":"publisher","award":["61905182"],"award-info":[{"award-number":["61905182"]}],"id":[{"id":"10.13039\/501100019537","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100019537","name":"Science Fund for Distinguished Young Scholars of Hubei Provinc","doi-asserted-by":"publisher","award":["62075200"],"award-info":[{"award-number":["62075200"]}],"id":[{"id":"10.13039\/501100019537","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100019537","name":"Science Fund for Distinguished Young Scholars of Hubei Provinc","doi-asserted-by":"publisher","award":["82002979"],"award-info":[{"award-number":["82002979"]}],"id":[{"id":"10.13039\/501100019537","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100019537","name":"Science Fund for Distinguished Young Scholars of Hubei Provinc","doi-asserted-by":"publisher","award":["2021CFA042"],"award-info":[{"award-number":["2021CFA042"]}],"id":[{"id":"10.13039\/501100019537","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100019537","name":"Science Fund for Distinguished Young Scholars of Hubei Provinc","doi-asserted-by":"publisher","award":["2020020601012237"],"award-info":[{"award-number":["2020020601012237"]}],"id":[{"id":"10.13039\/501100019537","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100019537","name":"Science Fund for Distinguished Young Scholars of Hubei Provinc","doi-asserted-by":"publisher","award":["2020BAB005"],"award-info":[{"award-number":["2020BAB005"]}],"id":[{"id":"10.13039\/501100019537","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100019537","name":"Science Fund for Distinguished Young Scholars of Hubei Provinc","doi-asserted-by":"publisher","award":["lcyf202010"],"award-info":[{"award-number":["lcyf202010"]}],"id":[{"id":"10.13039\/501100019537","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100019537","name":"Science Fund for Distinguished Young Scholars of Hubei Provinc","doi-asserted-by":"publisher","award":["ZNXKPY2022017"],"award-info":[{"award-number":["ZNXKPY2022017"]}],"id":[{"id":"10.13039\/501100019537","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Wuhan Research Program of Application Foundation and Advanced Technology","award":["61905182"],"award-info":[{"award-number":["61905182"]}]},{"name":"Wuhan Research Program of Application Foundation and Advanced Technology","award":["62075200"],"award-info":[{"award-number":["62075200"]}]},{"name":"Wuhan Research Program of Application Foundation and Advanced Technology","award":["82002979"],"award-info":[{"award-number":["82002979"]}]},{"name":"Wuhan Research Program of Application Foundation and Advanced Technology","award":["2021CFA042"],"award-info":[{"award-number":["2021CFA042"]}]},{"name":"Wuhan Research Program of Application Foundation and Advanced Technology","award":["2020020601012237"],"award-info":[{"award-number":["2020020601012237"]}]},{"name":"Wuhan Research Program of Application Foundation and Advanced Technology","award":["2020BAB005"],"award-info":[{"award-number":["2020BAB005"]}]},{"name":"Wuhan Research Program of Application Foundation and Advanced Technology","award":["lcyf202010"],"award-info":[{"award-number":["lcyf202010"]}]},{"name":"Wuhan Research Program of Application Foundation and Advanced Technology","award":["ZNXKPY2022017"],"award-info":[{"award-number":["ZNXKPY2022017"]}]},{"name":"The Key Research and Development Program of Hubei province","award":["61905182"],"award-info":[{"award-number":["61905182"]}]},{"name":"The Key Research and Development Program of Hubei province","award":["62075200"],"award-info":[{"award-number":["62075200"]}]},{"name":"The Key Research and Development Program of Hubei province","award":["82002979"],"award-info":[{"award-number":["82002979"]}]},{"name":"The Key Research and Development Program of Hubei province","award":["2021CFA042"],"award-info":[{"award-number":["2021CFA042"]}]},{"name":"The Key Research and Development Program of Hubei province","award":["2020020601012237"],"award-info":[{"award-number":["2020020601012237"]}]},{"name":"The Key Research and Development Program of Hubei province","award":["2020BAB005"],"award-info":[{"award-number":["2020BAB005"]}]},{"name":"The Key Research and Development Program of Hubei province","award":["lcyf202010"],"award-info":[{"award-number":["lcyf202010"]}]},{"name":"The Key Research and Development Program of Hubei province","award":["ZNXKPY2022017"],"award-info":[{"award-number":["ZNXKPY2022017"]}]},{"name":"2020 Medical Science and Technology Innovation Platform Support Project of Zhongnan Hospital of Wuhan University","award":["61905182"],"award-info":[{"award-number":["61905182"]}]},{"name":"2020 Medical Science and Technology Innovation Platform Support Project of Zhongnan Hospital of Wuhan University","award":["62075200"],"award-info":[{"award-number":["62075200"]}]},{"name":"2020 Medical Science and Technology Innovation Platform Support Project of Zhongnan Hospital of Wuhan University","award":["82002979"],"award-info":[{"award-number":["82002979"]}]},{"name":"2020 Medical Science and Technology Innovation Platform Support Project of Zhongnan Hospital of Wuhan University","award":["2021CFA042"],"award-info":[{"award-number":["2021CFA042"]}]},{"name":"2020 Medical Science and Technology Innovation Platform Support Project of Zhongnan Hospital of Wuhan University","award":["2020020601012237"],"award-info":[{"award-number":["2020020601012237"]}]},{"name":"2020 Medical Science and Technology Innovation Platform Support Project of Zhongnan Hospital of Wuhan University","award":["2020BAB005"],"award-info":[{"award-number":["2020BAB005"]}]},{"name":"2020 Medical Science and Technology Innovation Platform Support Project of Zhongnan Hospital of Wuhan University","award":["lcyf202010"],"award-info":[{"award-number":["lcyf202010"]}]},{"name":"2020 Medical Science and Technology Innovation Platform Support Project of Zhongnan Hospital of Wuhan University","award":["ZNXKPY2022017"],"award-info":[{"award-number":["ZNXKPY2022017"]}]},{"name":"Discipline Cultivation Funding, Zhongnan Hospital of Wuhan University","award":["61905182"],"award-info":[{"award-number":["61905182"]}]},{"name":"Discipline Cultivation Funding, Zhongnan Hospital of Wuhan University","award":["62075200"],"award-info":[{"award-number":["62075200"]}]},{"name":"Discipline Cultivation Funding, Zhongnan Hospital of Wuhan University","award":["82002979"],"award-info":[{"award-number":["82002979"]}]},{"name":"Discipline Cultivation Funding, Zhongnan Hospital of Wuhan University","award":["2021CFA042"],"award-info":[{"award-number":["2021CFA042"]}]},{"name":"Discipline Cultivation Funding, Zhongnan Hospital of Wuhan University","award":["2020020601012237"],"award-info":[{"award-number":["2020020601012237"]}]},{"name":"Discipline Cultivation Funding, Zhongnan Hospital of Wuhan University","award":["2020BAB005"],"award-info":[{"award-number":["2020BAB005"]}]},{"name":"Discipline Cultivation Funding, Zhongnan Hospital of Wuhan University","award":["lcyf202010"],"award-info":[{"award-number":["lcyf202010"]}]},{"name":"Discipline Cultivation Funding, Zhongnan Hospital of Wuhan University","award":["ZNXKPY2022017"],"award-info":[{"award-number":["ZNXKPY2022017"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>Breast cancer is one of the most common types of cancer and is the leading cause of cancer-related death. Diagnosis of breast cancer is based on the evaluation of pathology slides. In the era of digital pathology, these slides can be converted into digital whole slide images (WSIs) for further analysis. However, due to their sheer size, digital WSIs diagnoses are time consuming and challenging. In this study, we present a lightweight architecture that consists of a bilinear structure and MobileNet-V3 network, bilinear MobileNet-V3 (BM-Net), to analyze breast cancer WSIs. We utilized the WSI dataset from the ICIAR2018 Grand Challenge on Breast Cancer Histology Images (BACH) competition, which contains four classes: normal, benign, in situ carcinoma, and invasive carcinoma. We adopted data augmentation techniques to increase diversity and utilized focal loss to remove class imbalance. We achieved high performance, with 0.88 accuracy in patch classification and an average 0.71 score, which surpassed state-of-the-art models. Our BM-Net shows great potential in detecting cancer in WSIs and is a promising clinical tool.<\/jats:p>","DOI":"10.3390\/bioengineering9060261","type":"journal-article","created":{"date-parts":[[2022,6,21]],"date-time":"2022-06-21T01:43:27Z","timestamp":1655775807000},"page":"261","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":39,"title":["BM-Net: CNN-Based MobileNet-V3 and Bilinear Structure for Breast Cancer Detection in Whole Slide Images"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7398-6059","authenticated-orcid":false,"given":"Jin","family":"Huang","sequence":"first","affiliation":[{"name":"The Institute of Technological Sciences, Wuhan University, Wuhan 430072, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2555-9199","authenticated-orcid":false,"given":"Liye","family":"Mei","sequence":"additional","affiliation":[{"name":"The Institute of Technological Sciences, Wuhan University, Wuhan 430072, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mengping","family":"Long","sequence":"additional","affiliation":[{"name":"The Institute of Technological Sciences, Wuhan University, Wuhan 430072, China"},{"name":"Department of Pathology, Peking University Cancer Hospital, Beijing 100142, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7239-2360","authenticated-orcid":false,"given":"Yiqiang","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Pathology, Peking University Cancer Hospital, Beijing 100142, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Sun","sequence":"additional","affiliation":[{"name":"Department of Pathology, Peking University Cancer Hospital, Beijing 100142, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoxiao","family":"Li","sequence":"additional","affiliation":[{"name":"The Institute of Technological Sciences, Wuhan University, Wuhan 430072, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hui","family":"Shen","sequence":"additional","affiliation":[{"name":"The Department of Hematology, Zhongnan Hospital, Wuhan University, Wuhan 430071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fuling","family":"Zhou","sequence":"additional","affiliation":[{"name":"The Department of Hematology, Zhongnan Hospital, Wuhan University, Wuhan 430071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaolan","family":"Ruan","sequence":"additional","affiliation":[{"name":"Department of Hematology, Renmin Hospital, Wuhan University, Wuhan 430071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0098-7613","authenticated-orcid":false,"given":"Du","family":"Wang","sequence":"additional","affiliation":[{"name":"The Institute of Technological Sciences, Wuhan University, Wuhan 430072, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8651-4795","authenticated-orcid":false,"given":"Shu","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Breast Surgery, Peking University People\u2019s Hospital, Beijing 100044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5124-7167","authenticated-orcid":false,"given":"Taobo","family":"Hu","sequence":"additional","affiliation":[{"name":"The Institute of Technological Sciences, Wuhan University, Wuhan 430072, China"},{"name":"Department of Breast Surgery, Peking University People\u2019s Hospital, Beijing 100044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cheng","family":"Lei","sequence":"additional","affiliation":[{"name":"The Institute of Technological Sciences, Wuhan University, Wuhan 430072, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"7","DOI":"10.3322\/caac.21708","article-title":"Cancer statistics, 2022","volume":"72","author":"Siegel","year":"2022","journal-title":"CA Cancer J. Clin."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1122","DOI":"10.1001\/jama.2015.1405","article-title":"Diagnostic Concordance Among Pathologists Interpreting Breast Biopsy Specimens","volume":"313","author":"Elmore","year":"2015","journal-title":"JAMA"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"680","DOI":"10.1136\/jclinpath-2017-204979","article-title":"Breast cancer histologic grading using digital microscopy: Concordance and outcome association","volume":"71","author":"Rakha","year":"2018","journal-title":"J. Clin. Pathol."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Senousy, Z., Abdelsamea, M.M., Mohamed, M.M., and Gaber, M.M. (2021). 3E-Net: Entropy-Based Elastic Ensemble of Deep Convolutional Neural Networks for Grading of Invasive Breast Carcinoma Histopathological Microscopic Images. Entropy, 23.","DOI":"10.3390\/e23050620"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"e194337","DOI":"10.1001\/jamanetworkopen.2019.4337","article-title":"Trends in the US and Canadian Pathologist Workforces from 2007 to 2017","volume":"2","author":"Metter","year":"2019","journal-title":"JAMA Netw. Open"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"108ra113","DOI":"10.1126\/scitranslmed.3002564","article-title":"Systematic Analysis of Breast Cancer Morphology Uncovers Stromal Features Associated with Survival","volume":"3","author":"Beck","year":"2011","journal-title":"Sci. Transl. Med."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Kanavati, F., and Tsuneki, M. (2021). Breast Invasive Ductal Carcinoma Classification on Whole Slide Images with Weakly-Supervised and Transfer Learning. Cancers, 13.","DOI":"10.1101\/2021.07.06.451320"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"3585","DOI":"10.3390\/curroncol28050307","article-title":"Detection of Cervical Cancer Cells in Whole Slide Images Using Deformable and Global Context Aware Faster RCNN-FPN","volume":"28","author":"Li","year":"2021","journal-title":"Curr. Oncol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1080\/10408363.2018.1536111","article-title":"Deep learning for image analysis: Personalizing medicine closer to the point of care","volume":"56","author":"Xie","year":"2019","journal-title":"Crit. Rev. Clin. Lab. Sci."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Chintalapudi, N., Battineni, G., Hossain, M.A., and Amenta, F. (2022). Cascaded Deep Learning Frameworks in Contribution to the Detection of Parkinson\u2019s Disease. Bioengineering, 9.","DOI":"10.3390\/bioengineering9030116"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Bizzego, A., Gabrieli, G., Neoh, M.J.Y., and Esposito, G. (2021). Improving the Efficacy of Deep-Learning Models for Heart Beat Detection on Heterogeneous Datasets. Bioengineering, 8.","DOI":"10.3390\/bioengineering8120193"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Li, P., Chen, X., and Shen, S. (2019, January 16\u201320). Stereo R-CNN Based 3D Object Detection for Autonomous Driving. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00783"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2685","DOI":"10.1109\/TMI.2020.3046672","article-title":"Self-Supervised Attention Mechanism for Pediatric Bone Age Assessment with Efficient Weak Annotation","volume":"40","author":"Liu","year":"2020","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Islam, M.M., Poly, T.N., Walther, B.A., Lin, M.C., and Li, Y.C. (2021). Artificial Intelligence in Gastric Cancer: Identifying Gastric Cancer Using Endoscopic Images with Convolutional Neural Network. Cancers, 13.","DOI":"10.3390\/cancers13215253"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Hamamoto, R., Suvarna, K., Yamada, M., Kobayashi, K., Shinkai, N., Miyake, M., Takahashi, M., Jinnai, S., Shimoyama, R., and Sakai, A. (2020). Application of Artificial Intelligence Technology in Oncology: Towards the Establishment of Precision Medicine. Cancers, 12.","DOI":"10.3390\/cancers12123532"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Adedigba, A.P., Adeshina, S.A., and Aibinu, A.M. (2022). Performance Evaluation of Deep Learning Models on Mammogram Classification Using Small Dataset. Bioengineering, 9.","DOI":"10.3390\/bioengineering9040161"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1007\/s00292-016-0182-8","article-title":"Histological grading of breast cancer","volume":"37","author":"Christgen","year":"2016","journal-title":"Pathologe"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.media.2015.12.002","article-title":"Automated classification of brain tumor type in whole-slide digital pathology images using local representative tiles","volume":"30","author":"Barker","year":"2016","journal-title":"Med. Image Anal."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1504","DOI":"10.1038\/s41598-020-58467-9","article-title":"Deep Learning Models for Histopathological Classification of Gastric and Colonic Epithelial Tumours","volume":"10","author":"Iizuka","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Vidyarthi, A., Shad, J., Sharma, S., and Agarwal, P. (2019, January 8\u201310). Classification of Breast Microscopic Imaging using Hybrid CLAHE-CNN Deep Architecture. Proceedings of the 2019 Twelfth International Conference on Contemporary Computing, Noida, India.","DOI":"10.1109\/IC3.2019.8844937"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Ara\u00fajo, T., Aresta, G., Castro, E., Rouco, J., Aguiar, P., Eloy, C., Pol\u00f3nia, A., and Campilho, A. (2017). Classification of breast cancer histology images using Convolutional Neural Networks. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0177544"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Vesal, S., Ravikumar, N., Davari, A., Ellmann, S., and Maier, A. (2018, January 6). Classification of Breast Cancer Histology Images Using Transfer Learning. Proceedings of the International Conference Image Analysis and Recognition, P\u00f3voa de Varzim, Portugal.","DOI":"10.1007\/978-3-319-93000-8_92"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Ferreira, C.A., Melo, T., Sousa, P., Meyer, M.I., Shakibapour, E., Costa, P., and Campilho, A. (2018, January 6). Classification of Breast Cancer Histology Images Through Transfer Learning Using a Pre-trained Inception Resnet V2. Proceedings of the International Conference Image Analysis and Recognition, P\u00f3voa de Varzim, Portugal.","DOI":"10.1007\/978-3-319-93000-8_86"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Anupama, M., Sowmya, V., and Soman, K. (2019, January 4\u20136). Breast Cancer Classification using Capsule Network with Preprocessed Histology Images. Proceedings of the 2019 International Conference on Communication and Signal Processing, Chennai, India.","DOI":"10.1109\/ICCSP.2019.8698043"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Huang, Y., and Chung, A.C.S. (2018, January 16\u201320). Improving High Resolution Histology Image Classification with Deep Spatial Fusion Network. Proceedings of the Computational Pathology and Ophthalmic Medical Image Analysis, Granada, Spain.","DOI":"10.1007\/978-3-030-00949-6_3"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Ni, H., Liu, H., Wang, K., Wang, X., Zhou, X., and Qian, Y. (2019, January 13). WSI-Net: Branch-Based and Hierarchy-Aware Network for Segmentation and Classification of Breast Histopathological Whole-Slide Images. Proceedings of the International Workshop on Machine Learning in Medical Imaging, Shenzhen, China.","DOI":"10.1007\/978-3-030-32692-0_5"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Patil, S.M., Tong, L., and Wang, M.D. (2020, January 13\u201317). Generating Region of Interests for Invasive Breast Cancer in Histopathological Whole-Slide-Image. Proceedings of the 2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC), Madrid, Spain.","DOI":"10.1109\/COMPSAC48688.2020.0-174"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"213502","DOI":"10.1109\/ACCESS.2020.3040106","article-title":"Detection of Breast Cancer from Whole Slide Histopathological Images Using Deep Multiple Instance CNN","volume":"8","author":"Das","year":"2020","journal-title":"IEEE Access"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Galal, S., and Sanchez-Freire, V. (2018, January 6). Candy Cane: Breast Cancer Pixel-Wise Labeling with Fully Convolutional Densenets. Proceedings of the International Conference Image Analysis and Recognition, P\u00f3voa de Varzim, Portugal.","DOI":"10.1007\/978-3-319-93000-8_93"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.media.2019.05.010","article-title":"BACH: Grand challenge on breast cancer histology images","volume":"56","author":"Aresta","year":"2019","journal-title":"Med. Image Anal."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Kohl, M., Walz, C., Ludwig, F., Braunewell, S., and Baust, M. (2018, January 27\u201329). Assessment of Breast Cancer Histology Using Densely Connected Convolutional Networks. Proceedings of the International Conference Image Analysis and Recognition, P\u00f3voa de Varzim, Portugal.","DOI":"10.1007\/978-3-319-93000-8_103"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Marami, B., Prastawa, M., Chan, M., Donovan, M., Fernandez, G., and Zeineh, J. (2018, January 27\u201329). Ensemble Network for Region Identification in Breast Histopathology Slides. Proceedings of the International Conference Image Analysis and Recognition, P\u00f3voa de Varzim, Portugal.","DOI":"10.1007\/978-3-319-93000-8_98"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Ciga, O., Chen, J., and Martel, A. (2019, January 13\u201317). Multi-layer Domain Adaptation for Deep Convolutional Networks. Proceedings of the Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data, Shenzhen, China.","DOI":"10.1007\/978-3-030-33391-1_3"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Kwok, S. (2018, January 27\u201329). Multiclass Classification of Breast Cancer in Whole-Slide Images. Proceedings of the International Conference Image Analysis and Recognition, P\u00f3voa de Varzim, Portugal.","DOI":"10.1007\/978-3-319-93000-8_106"},{"key":"ref_35","unstructured":"(2022, April 12). ICIAR 2018 Challenge Grand Challenge. Available online: https:\/\/iciar2018-challenge.grand-challenge.org\/."},{"key":"ref_36","unstructured":"Howard, A., Sandler, M., Chu, G., Chen, L.C., Chen, B., Tan, M., Wang, W., Zhu, Y., Pang, R., and Vasudevan, V. (November, January 27). Searching for MobileNetV3. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Korea."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., RoyChowdhury, A., and Maji, S. (2015, January 7\u201313). Bilinear CNN Models for Fine-Grained Visual Recognition. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.170"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Xue, J., Zhang, H., and Dana, K. (2018, January 18\u201323). Deep Texture Manifold for Ground Terrain Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00065"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Goyal, P., Girshick, R., He, K., and Doll\u00e1r, P. (2017, January 22\u201329). Focal Loss for Dense Object Detection. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_40","unstructured":"Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017). MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.C. (2018, January 18\u201323). MobileNetV2: Inverted Residuals and Linear Bottlenecks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Aral, R.A., Keskin, S.R., Kaya, M., and Hac\u0131\u00f6mero\u011flu, M. (2018, January 10\u201313). Classification of TrashNet Dataset Based on Deep Learning Models. Proceedings of the 2018 IEEE International Conference on Big Data, Seattle, WA, USA.","DOI":"10.1109\/BigData.2018.8622212"},{"key":"ref_43","unstructured":"Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., and Antiga, L. (2019, January 8\u201314). PyTorch: An Imperative Style, High-Performance Deep Learning Library. Proceedings of the Advances in Neural Information Processing Systems, Vancouver, Canada."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Vu, Q.D., To, M.N.N., Kim, E., and Kwak, J.T. (2018, January 27\u201329). Micro and Macro Breast Histology Image Analysis by Partial Network Re-use. Proceedings of the International Conference Image Analysis and Recognition, P\u00f3voa de Varzim, Portugal.","DOI":"10.1007\/978-3-319-93000-8_102"}],"container-title":["Bioengineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5354\/9\/6\/261\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:35:34Z","timestamp":1760139334000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5354\/9\/6\/261"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,20]]},"references-count":44,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2022,6]]}},"alternative-id":["bioengineering9060261"],"URL":"https:\/\/doi.org\/10.3390\/bioengineering9060261","relation":{},"ISSN":["2306-5354"],"issn-type":[{"value":"2306-5354","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,20]]}}}