{"id":296,"date":"2025-10-09T14:11:35","date_gmt":"2025-10-09T05:11:35","guid":{"rendered":"https:\/\/www.med.osaka-u.ac.jp\/pub\/hp-info\/wordpress\/?post_type=achievement&p=296"},"modified":"2026-04-28T21:26:36","modified_gmt":"2026-04-28T12:26:36","slug":"paper-zhang-20251009","status":"publish","type":"achievement","link":"https:\/\/www.med.osaka-u.ac.jp\/pub\/hp-info\/achievement\/paper-zhang-20251009\/","title":{"rendered":"ILD-Slider: A Parameter-Efficient Model for Identifying Progressive Fibrosing Interstitial Lung Disease from Chest CT Slices"},"content":{"rendered":"\n

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Keywords<\/h4>\n\n\n\n

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\"\"
Overview of the proposed ILD-Slider framework. A PM extractor and PSM are used to identify the predefined PMs: apical lung, tracheal bifurcation, and upper diaphragm. To address severe class imbalance, the PM extractor is trained on a class-balanced dataset constructed by oversampling the PM classes and undersampling the Non-PM class. Based on the extracted PMs, RSs are selected for subsequent PF-ILD identification. Finally, a lightweight slice-level 3D Adapter (Slider), equipped with tunable adapters in each transformer block, performs the final PF-ILD identification.<\/figcaption><\/figure>\n\n\n\n

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