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However, the highly variable shapes and appearances of lesions pose substantial challenges for automated\u00a0segmentation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To tackle these challenges, we propose a deep\u2010learning model, the Retinal Feature Enhancement Network (RFENet), designed to improve the accuracy and robustness of retinal lesion segmentation under challenging imaging\u00a0conditions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>RFENet builds upon the UNeXt backbone and introduces two modules tailored for retinal lesion segmentation: an Adaptive Feature Refinement Unit (AFRU), which selectively emphasizes informative features, and an Optimized Channel\u2010Wise Convolution Unit (OCCU), which captures fine structural details in irregular lesions. Two expert\u2010annotated datasets were used: 1070 OCT B\u2010scans from 83 CNV patients and 184 FFA images from 107 CNP patients. Images were split at the patient level into training (70%), validation (10%), and test (20%) sets. Performance was compared against state\u2010of\u2010the\u2010art segmentation models, including UNet++, Swin\u2010UNet, SelfReg\u2010UNet, DAEFormer, and Mamba\u2010UNet. Evaluation metrics included Dice coefficient and Intersection over Union (IoU). Statistical significance was assessed using paired two\u2010tailed t\u2010tests (), and effect sizes were reported with Cohen's .<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      On the CNV dataset, RFENet achieved a Dice of 82.50% and an IoU of 71.66%, with clear improvements over competing models. On the CNP dataset, RFENet achieved a Dice of 74.43% and an IoU of 60.80%, slightly surpassing the best benchmark. Most pairwise comparisons were statistically significant, including CNV compared with Swin\u2010UNet and SelfReg\u2010UNet (\n                      <jats:italic>p<\/jats:italic>\n                      0.001) and CNP compared with DAEFormer (\n                      <jats:italic>p<\/jats:italic>\n                      0.035). Statistical analyses, including effect size evaluation, further confirmed the robustness of these improvements, with the most notable gains observed in the CNV\u00a0dataset.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>\n                      RFENet demonstrated consistent improvements over strong benchmarks on both CNV and CNP tasks, with statistical evidence supporting the reliability of these gains. These results indicate that RFENet provides a reliable technical advance for automated segmentation of CNV and CNP lesions associated with AMD. To facilitate reproducibility and further research, the source code is publicly available at\n                      <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/HaoSun223\/RFENet\">https:\/\/github.com\/HaoSun223\/RFENet<\/jats:ext-link>\n                      .\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.70441","type":"journal-article","created":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T13:07:06Z","timestamp":1776344826000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Refining feature representation for accurate fundus lesion segmentation"],"prefix":"10.1002","volume":"53","author":[{"given":"Hao","family":"Sun","sequence":"first","affiliation":[{"name":"School of Future Science and Engineering Soochow University Suzhou 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