A stereo spatial decoupling network for medical image classification

Autor: Hongfeng You, Long Yu, Shengwei Tian, Weiwei Cai
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: Complex & Intelligent Systems, Vol 9, Iss 5, Pp 5965-5974 (2023)
Druh dokumentu: article
ISSN: 2199-4536
2198-6053
DOI: 10.1007/s40747-023-01049-9
Popis: Abstract 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.
Databáze: Directory of Open Access Journals
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