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FleXray: Universal Clinical X-ray Segmentation

Paper recorded by Signals 4 on 2026-09-22 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-22 on arXiv · recorded by Signals 4 on 2026-09-23

Category: cs.AI · 人工智能 · first seen 2026-09-23

Abstract

X-ray is medicine's most widely used imaging modality, yet remains among its least quantitative. Unlike volumetric modalities like CT or MRI, X-ray collapses 3D anatomy into a 2D projection, causing structures to overlap and anatomical boundaries to be ambiguous, even to experts. As a result, labeling X-ray databases for training general-purpose segmentation systems is impractical, leaving morphom

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#7 most recent of 360 cs.AI papers we have recorded · ↑ newer: Type-Safe Is Not Error-Free: A Constrained Decision Head Follows the O · ↓ older: Metrics Failure in LLM-Based Code Vulnerability Repair: An Empirical S
Cite this page: FleXray: Universal Clinical X-ray Segmentation: the #7 most recent of 360 cs.AI papers we have recorded (as of 2026-09-22). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/flexray-universal-clinical-x-ray-segmentation.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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