{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T04:52:18Z","timestamp":1787028738264,"version":"build-2736575974"},"reference-count":44,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,12,29]],"date-time":"2022-12-29T00:00:00Z","timestamp":1672272000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["62206242"],"award-info":[{"award-number":["62206242"]}]},{"name":"National Natural Science Foundation of China","award":["2019YFC0118404"],"award-info":[{"award-number":["2019YFC0118404"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JPM"],"abstract":"<jats:p>Eyelid tumors are tumors that occur in the eye and its appendages, affecting vision and appearance, causing blindness and disability, and some having a high lethality rate. Pathological images of eyelid tumors are characterized by large pixels, multiple scales, and similar features. Solving the problem of difficult and time-consuming fine-grained classification of pathological images is important to improve the efficiency and quality of pathological diagnosis. The morphology of Basal Cell Carcinoma (BCC), Meibomian Gland Carcinoma (MGC), and Cutaneous Melanoma (CM) in eyelid tumors are very similar, and it is easy to be misdiagnosed among each category. In addition, the diseased area, which is decisive for the diagnosis of the disease, usually occupies only a relatively minor portion of the entire pathology section, and screening the area of interest is a tedious and time-consuming task. In this paper, deep learning techniques to investigate the pathological images of eyelid tumors. Inspired by the knowledge distillation process, we propose the Multiscale-Attention-Crop-ResNet (MAC-ResNet) network model to achieve the automatic classification of three malignant tumors and the automatic localization of whole slide imaging (WSI) lesion regions using U-Net. The final accuracy rates of the three classification problems of eyelid tumors on MAC-ResNet were 96.8%, 94.6%, and 90.8%, respectively.<\/jats:p>","DOI":"10.3390\/jpm13010089","type":"journal-article","created":{"date-parts":[[2022,12,30]],"date-time":"2022-12-30T03:31:17Z","timestamp":1672371077000},"page":"89","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["MAC-ResNet: Knowledge Distillation Based Lightweight Multiscale-Attention-Crop-ResNet for Eyelid Tumors Detection and Classification"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3971-8434","authenticated-orcid":false,"given":"Xingru","family":"Huang","sequence":"first","affiliation":[{"name":"College of Media Engineering, Communication University of Zhejiang, Hangzhou 310042, China"},{"name":"School of Electronic Engineering and Computer Science, Queen Mary University of London, Mile End Road, London E1 4NS, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunlei","family":"Yao","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou 310009, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Feng","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310005, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lingxiao","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310005, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huaqiong","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Media Engineering, Communication University of Zhejiang, Hangzhou 310042, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaodiao","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Media Engineering, Communication University of Zhejiang, Hangzhou 310042, China"},{"name":"School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310005, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juan","family":"Ye","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou 310009, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4627-3392","authenticated-orcid":false,"given":"Yaqi","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Media Engineering, Communication University of Zhejiang, Hangzhou 310042, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Singh, A.D., and Seregard, S. 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