mlsanityData to 7 Oct 2026

HyperCT: Low-Rank Hypernet for Unified Chest CT Analysis

arXiv 2604.03224 ↗First posted 3 Apr 2026eess.IV cs.CVcited by 0

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Abstract (arXiv)

Non-contrast chest CTs offer a rich opportunity for both conventional pulmonary and opportunistic extra-pulmonary screening. While Multi-Task Learning (MTL) can unify these diverse tasks, standard hard-parameter sharing approaches are often suboptimal for modeling distinct pathologies. We propose HyperCT, a framework that dynamically adapts a Vision Transformer backbone via a Hypernetwork. To ensure computational efficiency, we integrate Low-Rank Adaptation (LoRA), allowing the model to regress task-specific low-rank weight updates rather than full parameters. Validated on a large-scale dataset of radiological and cardiological tasks, method outperforms various strong baselines, offering a unified, parameter-efficient solution for holistic patient assessment. Our code is available at https://github.com/lfb-1/HyperCT.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 27 of its 45 references that are in the map. First posted on arXiv 3 Apr 2026.

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27 of 45 references.

References · 27

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15 more
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