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The high-level refinement decoder uses dual attention mechanism to integrate the same-level features in the two encoders with the output of the low-level attention refinement module for multiscale information fusion, which focus the model on the lesion area to generate accurate predictions. We evaluated the segmentation performance of the proposed CARNet on the IDRiD, E-ophtha and DDR data sets. Extensive comparison experiments and ablation studies on various data sets demonstrate the proposed framework outperforms the state-of-the-art approaches and has better accuracy and robustness. It not only overcomes the interference of similar tissues and noises to achieve accurate multi-lesion segmentation, but also preserves the contour details and shape features of small lesions without overloading GPU memory usage.<\/jats:p>","DOI":"10.1007\/s40747-021-00630-4","type":"journal-article","created":{"date-parts":[[2022,1,4]],"date-time":"2022-01-04T07:03:04Z","timestamp":1641279784000},"page":"1681-1701","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":37,"title":["CARNet: Cascade attentive RefineNet for multi-lesion segmentation of diabetic retinopathy images"],"prefix":"10.1007","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1652-3861","authenticated-orcid":false,"given":"Yanfei","family":"Guo","sequence":"first","affiliation":[]},{"given":"Yanjun","family":"Peng","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,1,4]]},"reference":[{"issue":"4","key":"630_CR1","doi-asserted-by":"publisher","first-page":"260","DOI":"10.1111\/ceo.12696","volume":"44","author":"DSW Ting","year":"2016","unstructured":"Ting DSW, Cheung GCM, Wong TY (2016) Diabetic retinopathy: global prevalence, major risk factors, screening practices and public health challenges: a review. 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