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arXiv · 2508.09478

GazeLT: Visual attention-guided long-tailed disease classification in chest radiographs

Moinak Bhattacharya·Gagandeep Singh·Shubham Jain·Prateek Prasanna
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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.

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Moinak Bhattacharya, Gagandeep Singh, Shubham Jain, Prateek Prasanna. 2025-08-13. GazeLT: Visual attention-guided long-tailed disease classification in chest radiographs. https://doi.org/10.59275/j.melba.2026-d8c8

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