arXiv Machine Learning July 23, 2026 By Renjie He

When Does Consensus Beat Voting? A Critical Analysis of Statistical Label Fusion in Medical Image Segmentation

Read the original on arXiv Machine Learning →

arXiv:2607. 19402v1 Announce Type: new Abstract: This paper provides a rigorous, self-contained investigation of consensus segmentation.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

computer-vision

arXiv AI

Jul 8

Volumetric Directional Diffusion: Anchoring Uncertainty Quantification in Anatomical Consensus for Ambiguous Medical Image Segmentation

arXiv:2603. 04024v2 Announce Type: replace-cross Abstract: Ambiguous 3D medical image segmentation often involves boundaries where different expert delineations are non-identical yet clinically plausible.

By Chao Wu, Mahesh Bhosale, Kangxian Xie, Pouya Karimian, David Doermann, Mingchen Gao

diffusion computer-vision safety

arXiv AI

Jun 30

Set-Inclusive Uncertainty Modeling for Robust Brain Tumor Segmentation

arXiv:2606. 30374v1 Announce Type: cross Abstract: Multimodal MRI is essential for accurate brain tumor segmentation.

By Seunghun Baek, Jihwan Park, Jaeyoon Sim, Hoseok Lee, Seungjoo Lee, Won Hwa Kim

computer-vision multimodal

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

computer-vision benchmarks

arXiv AI

1d ago

MID: Mask-Image Distributional Divergence for Evaluating Medical Image Segmentation

arXiv:2610.17354v1 Announce Type: cross Abstract: Current evaluation protocols for medical image segmentation remain largely anchored in sample-wise comparisons, e.g., Dice and Hausdorff distance, wh...

By Vincenzo Marcian\`o, Xiaoming Zhang, Gianluca Guglielmo, Sebastien Ourselin, Michela Antonelli, Maria A. Zuluaga

computer-vision benchmarks

arXiv Computer Vision

Oct 8

Quantifying Volumetric Risk: Class-Aware Asymmetric Weighted Conformal Prediction for 3D Medical Image Segmentation

arXiv:2610.09392v1 Announce Type: new Abstract: Reliable volumetric segmentation is critical for clinical diagnostics, yet foundation models such as MedSAM remain deterministic and lack calibrated un...

By Shadi Alijani, Fereshteh Aghaee Meibodi, Homayoun Najjaran

llms computer-vision multimodal benchmarks

arXiv Computer Vision

Aug 25

Image-Conditioned Diffusion Models for Quality Assurance of Organ-at-Risk Segmentations in Radiotherapy

arXiv:2608.23432v1 Announce Type: new Abstract: Accurate organ-at-risk segmentation is essential for radiotherapy planning, but reviewing segmentations is time-consuming and subjective. We investigat...

By Clea Dronne, Catharine H Clark, Xavier Loizeau, Elizabeth Miles, Peter Hoskin, Jamie R McClelland

diffusion computer-vision

One email a morning, machine-written

Related stories

Volumetric Directional Diffusion: Anchoring Uncertainty Quantification in Anatomical Consensus for Ambiguous Medical Image Segmentation

Set-Inclusive Uncertainty Modeling for Robust Brain Tumor Segmentation

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

MID: Mask-Image Distributional Divergence for Evaluating Medical Image Segmentation

Quantifying Volumetric Risk: Class-Aware Asymmetric Weighted Conformal Prediction for 3D Medical Image Segmentation

Image-Conditioned Diffusion Models for Quality Assurance of Organ-at-Risk Segmentations in Radiotherapy