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ARC-CT: Anatomy-Routed Contrastive Vision-Language Learning for 3D Chest CT

Paper recorded by Signals 4 on 2026-08-28 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-08-28 on arXiv · recorded by Signals 4 on 2026-08-31

Category: cs.AI · 人工智能 · first seen 2026-08-31

Abstract

Contrastive vision-language learning uses paired chest CT volumes and radiology reports to learn abnormality classifiers without manually annotated labels. However, two characteristics of chest CT challenge conventional global contrastive learning. First, many critical abnormalities are small or anatomically localized, and pooling an en- tire volume into a single embedding may dilute their visual

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#300 most recent of 300 cs.AI papers we have recorded · ↑ newer: Anatomy-Aware Promptable Segmentation with Online Interactive Training
Cite this page: ARC-CT: Anatomy-Routed Contrastive Vision-Language Learning for 3D Chest CT: the #300 most recent of 300 cs.AI papers we have recorded (as of 2026-08-28). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/arc-ct-anatomy-routed-contrastive-vision-language-learning-for-3d-chest-ct.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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