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Evidence record 7278 · automatically gathered

Fair Lung Disease Diagnosis from Chest CT via Gender-Adversarial Attention Multiple Instance Learning

We present a fairness-aware framework for multi-class lung disease diagnosis from chest CT volumes, developed for the Fair Disease Diagnosis Challenge at the PHAROS-AIF-MIH Workshop (CVPR 2026). The challenge requires classifying CT scans into four categories -- Healthy, COVID-19, Adenocarcinoma, and Squamous Cell Carcinoma -- with performance measured as the average of per-gender macro F1 scores, explicitly penalizing gender-inequitable predictions. Our approach addresses two core difficulties:

Record details

Published: 13 March 2026
Source: arXiv
Category: Research
Topics: Bias & fairness · Healthcare
Retrieved: 14 July 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

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How to cite this record

ethics.ai (13 March 2026), “Fair Lung Disease Diagnosis from Chest CT via Gender-Adversarial Attention Multiple Instance Learning,” evidence record 7278, https://ethics.ai/record/7278 (originally published by arXiv).

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