aceLearning global and local representations for generalized category discovery in neurodegenerative diseases
Abstract Background Artificial intelligence (AI) has achieved notable success in closed‐set classification of neurodegenerative diseases, but its performance degrades significantly in more realistic open‐set settings, such as discovering rare or novel disease subtypes from unlabeled data that are not seen during training. Purpose This study introduces, to our knowledge, the first framework for generalized category discovery (GCD) in neurodegenerative diseases using MRI. It aims to leverage labeled data from know…
A new MRI-based category discovery framework outperformed state-of-the-art methods at identifying novel neurodegenerative disease categories in unlabeled data, using labeled data from known categories.
Performance on novel categories is modest, notably 16.67% normalized mutual information, and results come from a benchmark dataset rather than clinical use.
Evidence
- Peer-reviewedMedical Physics2026-10-22
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Truvace Impact Record TRV-2026-2187, v1: “Learning global and local representations for generalized category discovery in neurodegenerative diseases.” Truvace, 2026-10-22. /record/TRV-2026-2187 (accessed at citation time). sha256 c3f21b7f162344d1…
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