Signals 4 · free daily AI digest

RT-Super: Learning Tumor Segmentation from Longitudinal Images and Reports

Paper recorded by Signals 4 on 2026-09-28 in cs.CV. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-28 on arXiv · recorded by Signals 4 on 2026-09-29

Category: cs.CV · 计算机视觉 · first seen 2026-09-29

Abstract

Multi-tumor segmentation is important for early cancer detection and allows radiologists to visualize, verify, and understand AI predictions. However, tumor segmentation masks are expensive, time-consuming, and unavailable for many tumor types in public data. Instead, hospitals have vast, readily available data that can guide segmentation: radiology reports, longitudinal images, and multi-phase im

Read on arXiv →

#14 most recent of 342 cs.CV papers we have recorded · ↑ newer: Verifiable Visual Rewards Transfer from Synthetic Scenes to Natural Pr · ↓ older: EvolvingAvatar: Interactive 3D Head Generation That Adapts as Conversa
Cite this page: RT-Super: Learning Tumor Segmentation from Longitudinal Images and Reports: the #14 most recent of 342 cs.CV papers we have recorded (as of 2026-09-28). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/rt-super-learning-tumor-segmentation-from-longitudinal-images-and-reports.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
Related: More cs.CV papers · arXiv signals · All papers · Today in AI
Get 4 AI signals a day by email — free.
Subscribe free → See all plans →
Get 4 AI signals a day by email — free
All models · All repos · By company · Daily editions