{"ID":2882619,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.09478","arxiv_id":"2508.09478","title":"GazeLT: Visual attention-guided long-tailed disease classification in chest radiographs","abstract":"In this work, we present GazeLT, a human visual attention integration-disintegration approach for long-tailed disease classification. A radiologist's eye gaze has distinct patterns that capture both fine-grained and coarser level disease related information. While interpreting an image, a radiologist's attention varies throughout the duration; it is critical to incorporate this into a deep learning framework to improve automated image interpretation. Another important aspect of visual attention is that apart from looking at major/obvious disease patterns, experts also look at minor/incidental findings (few of these constituting long-tailed classes) during the course of image interpretation. GazeLT harnesses the temporal aspect of the visual search process, via an integration and disintegration mechanism, to improve long-tailed disease classification. We show the efficacy of GazeLT on two publicly available datasets for long-tailed disease classification, namely the NIH-CXR-LT (n=89237) and the MIMIC-CXR-LT (n=111898) datasets. GazeLT outperforms the best long-tailed loss by 4.1% and the visual attention-based baseline by 21.7% in average accuracy metrics for these datasets. Our code is available at https://github.com/lordmoinak1/gazelt.","short_abstract":"In this work, we present GazeLT, a human visual attention integration-disintegration approach for long-tailed disease classification. A radiologist's eye gaze has distinct patterns that capture both fine-grained and coarser level disease related information. While interpreting an image, a radiologist's attention varies...","url_abs":"https://arxiv.org/abs/2508.09478","url_pdf":"https://arxiv.org/pdf/2508.09478v1","authors":"[\"Moinak Bhattacharya\",\"Gagandeep Singh\",\"Shubham Jain\",\"Prateek Prasanna\"]","published":"2025-08-13T04:13:27Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false,"code_links":[{"ID":610907,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2882619,"paper_url":"https://arxiv.org/abs/2508.09478","paper_title":"GazeLT: Visual attention-guided long-tailed disease classification in chest radiographs","repo_url":"https://github.com/lordmoinak1/gazelt","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}