UNCERTAINTY-BASED ENSEMBLE LEARNING IN CMR SEMANTIC SEGMENTATION

Yiwei Liu; Liang Zhong; Lingyi Wen; Yuankai Wu

ICASSP 2026poster0 citations

UNCERTAINTY-BASED ENSEMBLE LEARNING IN CMR SEMANTIC SEGMENTATION

Yiwei Liu, Liang Zhong, Lingyi Wen, Yuankai Wu

Abstract

Existing methods derive clinical functional metrics from ventricular semantic segmentation in cardiac cine sequences. While performing well on overall segmentation, they struggle with the end slices. To address this, we extract global uncertainty from segmentation variance and use it in our ensemble learning method, Streaming, for classifier weighting, balancing overall and end-slice performance. We introduce the End Coefficient (EC) to quantify end-slice accuracy. Experiments on ACDC and M\&Ms datasets show that our framework achieves near state-of-the-art Dice Similarity Coefficient (DSC) and outperforms all models on end-slice performance, improving patient-specific segmentation accuracy. We open-sourced our code on https://github.com/LEw1sin/Uncertainty-Ensemble.

BibTeX

@inproceedings{icassp2026_uncertaintybased,
  title = {UNCERTAINTY-BASED ENSEMBLE LEARNING IN CMR SEMANTIC SEGMENTATION},
  author = {Yiwei Liu and Liang Zhong and Lingyi Wen and Yuankai Wu},
  booktitle = {ICASSP 2026},
  year = {2026}
}