MS-ANet: deep learning for automated multi-label thoracic disease detection and classification.

ABSTRACT: The chest X-ray is one of the most common radiological examination types for the diagnosis of chest diseases. Nowadays, the automatic classification technology of radiological images has been widely used in clinical diagnosis and treatment plans. However, each disease has its own different response characteristic receptive field region, which is the main challenge for chest disease classification tasks. Besides, the imbalance of sample data categories further increases the difficulty of tasks. To solve these problems, we propose a new multi-label chest disease image classification scheme based on a multi-scale attention network. In this scheme, multi-scale information is iteratively fused to focus on regions with a high probability of disease, to effectively mine more meaningful informatio

SUBMITTER: Xu J 

PROVIDER: S-EPMC8157016 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature