{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T14:40:02Z","timestamp":1755873602145,"version":"3.44.0"},"publisher-location":"New York, NY, USA","reference-count":18,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,10,20]],"date-time":"2023-10-20T00:00:00Z","timestamp":1697760000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,10,20]]},"DOI":"10.1145\/3644116.3644142","type":"proceedings-article","created":{"date-parts":[[2024,4,5]],"date-time":"2024-04-05T18:38:22Z","timestamp":1712342302000},"page":"136-140","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Research Progress of Deep Learning Based Medical Image Classification Techniques"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-2490-7363","authenticated-orcid":false,"given":"Chenlu","family":"Lin","sequence":"first","affiliation":[{"name":"School of Advanced Manufacturing, Fuzhou University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,4,5]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"crossref","unstructured":"Szegedy C. Liu W. Jia Y. 2014. Going Deeper with Convolutions. CoRR.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"crossref","unstructured":"He K. Zhang X. Ren S. 2015. Deep Residual Learning for Image Recognition. CoRR.","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"crossref","unstructured":"Xie S. Girshick R. Doll\u00e1 P. 2016. Aggregated Residual Transformations for Deep Neural Networks. CoRR.","DOI":"10.1109\/CVPR.2017.634"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"crossref","unstructured":"Huang G. Liu Z. Kilian Q. 2016. Densely Connected Convolutional Networks. CoRR.","DOI":"10.1109\/CVPR.2017.243"},{"key":"e_1_3_2_1_5_1","unstructured":"Howard A.G. Zhu M. Chen B. 2017. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision. Applications arxiv:1704.04861."},{"key":"e_1_3_2_1_6_1","unstructured":"Tan M. Quoc V. 2019. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. CoRR."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.3844\/jcssp.2020.620.625"},{"key":"e_1_3_2_1_8_1","first-page":"13433","volume":"2022","author":"Wang K.","year":"2022","unstructured":"Wang, K., 2022. FFCNet: Fourier Transform-Based Frequency Learning and Complex Convolutional Network for Colon Disease Classification. In: MICCAI 2022, vol 13433.","journal-title":"MICCAI"},{"volume-title":"Classification of Breast Cancer Histology Images Using Multi-Size and Discriminative Patches Based on Deep Learning","author":"Li Y.","key":"e_1_3_2_1_9_1","unstructured":"Li, Y., Wu, J., Wu, Q. 2019. Classification of Breast Cancer Histology Images Using Multi-Size and Discriminative Patches Based on Deep Learning. IEEE Access, l."},{"key":"e_1_3_2_1_10_1","first-page":"109","article-title":"Transfer Learning in Breast Mammogram Abnormalities Classification with Mobilenet and Nasnet. In: 2019 IWSSIP, Osijek","author":"Falcon\u00ed L.G.","year":"2019","unstructured":"Falcon\u00ed, L.G., P\u00e9rez, M., Aguilar, W.G. 2019. Transfer Learning in Breast Mammogram Abnormalities Classification with Mobilenet and Nasnet. In: 2019 IWSSIP, Osijek, Croatia, 109-114.","journal-title":"Croatia"},{"key":"e_1_3_2_1_11_1","first-page":"10882","article-title":"Hierarchical ResNeXt Models for Breast Cancer Histology Image Classification. In","volume":"2018","author":"Kon\u00e9 I.","year":"2018","unstructured":"Kon\u00e9, I., Boulmane, L. 2018. Hierarchical ResNeXt Models for Breast Cancer Histology Image Classification. In: ICIAR 2018, vol 10882.","journal-title":"ICIAR"},{"volume-title":"Medical Image Classification Using a Light-Weighted Hybrid Neural Network Based on PCANet and DenseNet","author":"Huang Z.","key":"e_1_3_2_1_12_1","unstructured":"Huang, Z., Zhu, X., Ding, M., 2020. Medical Image Classification Using a Light-Weighted Hybrid Neural Network Based on PCANet and DenseNet. IEEE Access."},{"key":"e_1_3_2_1_13_1","unstructured":"Gon\u00e7alo M. Deevyankar A. Isabel de la Torre-D\u00edez. 2020. Automated medical diagnosis of COVID-19 through EfficientNet convolutional neural network. Applied Soft Computing Journal."},{"key":"e_1_3_2_1_14_1","unstructured":"Sudhakar T. Seifedine K. Ahmed N. 2023. An Explainable Classification Method Based on Complex Scaling in Histopathology Images for Lung and Colon Cancer. Diagnostics 9."},{"key":"e_1_3_2_1_15_1","unstructured":"Louis J. V. Wei J. Hassanpour S. 2019. Generative Image Translation for Data Augmentation in Colorectal Histopathology Images. CoRR."},{"key":"e_1_3_2_1_16_1","unstructured":"Fahdi K. Seiya M. Masayuki T. 2020. Weakly-supervised learning for lung carcinoma classification using deep learning. Scientific reports 1."},{"volume-title":"\u201cI don't know","author":"Laves M.","key":"e_1_3_2_1_17_1","unstructured":"Laves, M., Ihler, S., Ortmaier, T. 2019. Uncertainty quantification in computer-aided diagnosis: make your model say \u201cI don't know\u201d for ambiguous cases. In: MIDL, London, UK."},{"key":"e_1_3_2_1_18_1","unstructured":"Tan M. & Le Q. 2021. EfficientNetV2: Smaller Models and Faster Training.\u00a0ArXiv abs\/2104.00298."}],"event":{"name":"ISAIMS 2023: 2023 4th International Symposium on Artificial Intelligence for Medicine Science","acronym":"ISAIMS 2023","location":"Chengdu China"},"container-title":["Proceedings of the 2023 4th International Symposium on Artificial Intelligence for Medicine Science"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3644116.3644142","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3644116.3644142","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T14:06:10Z","timestamp":1755871570000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3644116.3644142"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,20]]},"references-count":18,"alternative-id":["10.1145\/3644116.3644142","10.1145\/3644116"],"URL":"https:\/\/doi.org\/10.1145\/3644116.3644142","relation":{},"subject":[],"published":{"date-parts":[[2023,10,20]]},"assertion":[{"value":"2024-04-05","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}