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Auton. Adapt. Syst."],"published-print":{"date-parts":[[2025,12,31]]},"abstract":"<jats:p>With the emergence of edge computing, there is a growing need for advanced technologies capable of real-time, efficient processing of complex data on edge devices, particularly in mobile health systems handling pathological images. On edge computing devices, the lightweighting of models and reduction of computational requirements not only save resources but also increase inference speed. Although many lightweight models and methods have been proposed in recent years, they still face many common challenges. This article introduces a novel convolution operation, Dynamic Scalable Convolution (DSC), which optimizes computational resources and accelerates inference on edge computing devices. DSC is shown to outperform traditional convolution methods in terms of parameter efficiency, computational speed, and overall performance, through comparative analyses in computer vision tasks like image classification and semantic segmentation. Experimental results demonstrate the significant potential of DSC in enhancing deep neural networks, particularly for edge computing applications in smart devices and remote healthcare, where it addresses the challenge of limited resources by reducing computational demands and improving inference speed. By integrating advanced convolution technology and edge computing applications, DSC offers a promising approach to support the rapidly developing mobile health field, especially in enhancing remote healthcare delivery through mobile multimedia communication.<\/jats:p>","DOI":"10.1145\/3732781","type":"journal-article","created":{"date-parts":[[2025,4,28]],"date-time":"2025-04-28T10:59:08Z","timestamp":1745837948000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Edge-Adaptive Dynamic Scalable Convolution for Efficient Remote Mobile Pathology Analysis"],"prefix":"10.1145","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8717-8375","authenticated-orcid":false,"given":"Peng","family":"Xiao","sequence":"first","affiliation":[{"name":"Network and Data Security Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0745-5836","authenticated-orcid":false,"given":"Dajiang","family":"Chen","sequence":"additional","affiliation":[{"name":"Network and Data Security Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7857-9719","authenticated-orcid":false,"given":"Zhen","family":"Qin","sequence":"additional","affiliation":[{"name":"Network and Data Security Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0691-2724","authenticated-orcid":false,"given":"Mingsheng","family":"Cao","sequence":"additional","affiliation":[{"name":"Network and Data Security Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1970-5743","authenticated-orcid":false,"given":"Ruidong","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China"}]}],"member":"320","published-online":{"date-parts":[[2025,11,26]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1108\/IJWIS-04-2023-0062"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1108\/IJWIS-10-2023-0184"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2023.3309321"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1108\/IJWIS-02-2022-0039"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1145\/3530192"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1145\/3487920"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1145\/3582080"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2019.2903245"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1145\/3391198"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11036-022-01998-4"},{"issue":"99","key":"e_1_3_2_12_2","first-page":"1","article-title":"Connectivity matters: Neural network pruning through the lens of effective sparsity","volume":"24","author":"Vysogorets Artem","year":"2023","unstructured":"Artem Vysogorets and Julia Kempe. 2023. 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