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Anatomy-aware Fine-grained Multimodal Fusion for Laryngopharyngeal Cancer T-Staging Prediction Using CT and Radiology Report

Paper recorded by Signals 4 on 2026-10-05 in cs.CV. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-05 on arXiv · recorded by Signals 4 on 2026-10-06

Category: cs.CV · 计算机视觉 · first seen 2026-10-06

Abstract

Accurate T-staging is crucial for guiding personalized treatment strategies for laryngopharyngeal cancer. However, current clinical practice relies on invasive biopsy procedures, whereas CT-based staging remains challenging due to the complex patterns of tumor invasion. Recent computer-aided approaches face two key challenges: 1) Structural relationship modeling: existing methods underrepresent an

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#353 most recent of 756 cs.CV papers we have recorded · ↑ newer: S2PD: Serial-to-Parallel Diffusion for Physically and Logically Consis · ↓ older: Less Context, Better Geometry: Masked Geometric Encoder for Robust 3D
Cite this page: Anatomy-aware Fine-grained Multimodal Fusion for Laryngopharyngeal Cancer T-Staging Prediction Using CT and Radiology Report: the #353 most recent of 756 cs.CV papers we have recorded (as of 2026-10-05). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/anatomy-aware-fine-grained-multimodal-fusion-for-laryngopharyngeal-cancer-t-stag.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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