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Segmentation Confidence for Arbitrary CNNs

Paper recorded by Signals 4 on 2026-10-27 in cs.CV. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-27 on arXiv · recorded by Signals 4 on 2026-10-28

Category: cs.CV · 计算机视觉 · first seen 2026-10-28

Abstract

Convolutional neural networks (CNN) are widely used for medical image segmentation. However, they lack an inherent measure of confidence. Existing methods for estimating prediction uncertainty often require a custom network architecture and/or dedicated training setups and need to be trained from scratch. In addition, the deduction of a single confidence score per input from the uncertainty maps i

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#122 most recent of 739 cs.CV papers we have recorded · ↑ newer: PerceptRL: Making Visual Perception Rewardable in MLLMs from Answers t · ↓ older: When Visual Evidence Is Insufficient: How Instructions, Post-Training,
Cite this page: Segmentation Confidence for Arbitrary CNNs: the #122 most recent of 739 cs.CV papers we have recorded (as of 2026-10-27). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/segmentation-confidence-for-arbitrary-cnns.html
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