arXiv AI
Jun 16

Federated Medical Image Segmentation under Real-World Label Noise: A Benchmark Suite for Noisy Label Learning Method Selection

arXiv:2606. 16868v1 Announce Type: cross Abstract: While federated learning (FL) enables collaborative medical image segmentation without centralizing sensitive data, real-world deployment is frequently complicated by cross-site label imperfections such as contour disagreement, missing or additional structures, and confused labels.

By Markus Bujotzek, Dimitrios Bounias, Stefan Denner, Ralf Floca, Maximilian Fischer, Peter Neher, Klaus Maier-Hein
arXiv Computer Vision
Aug 31

Report Supervision

The paper introduces Report Supervision (R‑Super), a framework that uses radiology reports to supervise tumor segmentation models. By incorporating loss functions that align segmentation outputs with report‑derived tumor counts, sizes, and locations, R‑Super improves detection and segmentation performance. Experiments on kidney and pancreatic tumors show up to a 15% increase in F1‑Score and DSC compared to mask‑only training, outperforming methods like CLIP and multi‑task learning.

By Pedro R. A. S. Bassia, Wenxuan Li, Jakob Wasserthal, Jieneng Chen, Xinze Zhou, Zheren Zhu, Chuntung Zhuanga, Sergio Decherchi, Andrea Cavalli, Kang Wang, Yang Yang, Alan Yuille, Zongwei Zhou
arXiv Computer Vision
Oct 22

SAM-DCE: Decoupled Context Enhancement for Prompt-Free Medical Image Segmentation

SAM-DCE is a framework that adapts the Segment Anything Model (SAM) for fully automatic, prompt‑free medical image segmentation. It introduces Spatial Representation Compensation (SRC) to recover fine anatomical details from intermediate Vision Transformer features and Semantic Token Calibration (STC) to enhance the consistency of foreground mask tokens. By refining spatial and semantic representations before using SAM’s original token‑to‑mask mechanism, SAM‑DCE achieves superior performance on the Synapse multi‑organ CT segmentation dataset, with a mean Dice score of 83.86% and an HD95 of 16.04 mm, outperforming several CNN, Transformer, and SAM baselines.

By Yingzhen Hu, Yiheng Zhong, Aijing Yang, Georges El Fakhri, Xiaofeng Liu
arXiv Machine Learning
Nov 2

Single-Annotation, Multi-Hypothesis Segmentation with Semantic-Spatial Residual Banks

The paper introduces the Semantic‑Spatial Residual Bank (SSRB) for single‑annotation, multi‑hypothesis segmentation, where each training image has only one mask but the model must generate a set of masks that cover possible human annotations at test time. SSRB combines a frozen semantic bank, trained with balanced assignments, with a residual learned from the image for each hypothesis. Experiments on five medical endpoints and the BSDS dataset show that SSRB improves coverage of held‑out masks compared to a Probabilistic U‑Net, produces more distinct useful masks on LIDC and LIDC‑hard, and maintains semantic grouping performance while its advantage grows with the number of returned masks.

By Armin Dadras, Robert Sablatnig, Franziska Proksa, Markus Seidl