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Syst."],"published-print":{"date-parts":[[2023,10]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Deep convolutional neural network (CNN) has made great progress in medical image classification. However, it is difficult to establish effective spatial associations, and always extracts similar low-level features, resulting in redundancy of information. To solve these limitations, we propose a stereo spatial discoupling network (TSDNets), which can leverage the multi-dimensional spatial details of medical images. Then, we use an attention mechanism to progressively extract the most discriminative features from three directions: horizontal, vertical, and depth. Moreover, a cross feature screening strategy is used to divide the original feature maps into three levels: important, secondary and redundant. Specifically, we design a cross feature screening module (CFSM) and a semantic guided decoupling module (SGDM) to model multi-dimension spatial relationships, thereby enhancing the feature representation capabilities. The extensive experiments conducted on multiple open source baseline datasets demonstrate that our TSDNets outperforms previous state-of-the-art models.<\/jats:p>","DOI":"10.1007\/s40747-023-01049-9","type":"journal-article","created":{"date-parts":[[2023,4,17]],"date-time":"2023-04-17T09:03:12Z","timestamp":1681722192000},"page":"5965-5974","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["A stereo spatial decoupling network for medical image classification"],"prefix":"10.1007","volume":"9","author":[{"given":"Hongfeng","family":"You","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9038-4129","authenticated-orcid":false,"given":"Long","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Shengwei","family":"Tian","sequence":"additional","affiliation":[]},{"given":"Weiwei","family":"Cai","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,4,17]]},"reference":[{"key":"1049_CR1","doi-asserted-by":"publisher","first-page":"106912","DOI":"10.1016\/j.asoc.2020.106912","volume":"98","author":"MF Aslan","year":"2021","unstructured":"Aslan MF, Unlersen MF, Sabanci K, Durdu A (2021) Cnn-based transfer learning-bilstm network: a novel approach for covid-19 infection detection. 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