Rethinking Uncertainty Quantification and Entanglement in Image Segmentation
Uncertainty quantification (UQ) is crucial in safety-critical applications such as medical image segmentation. Total uncertainty is typically decomposed into data-related aleatoric uncertainty (AU) and model-related epistemic uncertainty (EU). Many methods exist for modeling AU (such as Probabilistic UNet, Diffusion) and EU (such as ensembles, MC Dropout), but it is unclear how they interact when combined. Additionally, recent work has revealed substantial entanglement between AU and EU, undermining the interpretability and practical usefulness of the decomposition. We present a comprehensive empirical study covering a broad range of AU-EU model combinations, propose a metric to quantify uncertainty entanglement, and evaluate both across downstream UQ tasks. Ensembles consistently exhibit lower entanglement and superior performance. Softmax models usually beat other AU methods, except in calibration where the results are dataset-dependent. A softmax ensemble performs remarkably well on all tasks. Finally, we analyze potential sources of uncertainty entanglement and outline directions for mitigating this effect.
Code (0)
등록된 구현이 없습니다.
Tasks
Out-of-Distribution DetectionMedical Image SegmentationSimilar Papers 제목 키워드 기반
Benchmarking Uncertainty Disentanglement: Specialized Uncertainties for Specialized Tasks
Uncertainty quantification, once a singular task, has evolved into a spectrum of tasks, including abstained prediction, out-of-distribution detection, and aleatoric uncertainty quantification. The latest goal is disentan…
BenchmarkingDisentanglementOut-of-Distribution DetectionUncertainty QuantificationQUBIQ: Uncertainty Quantification for Biomedical Image Segmentation Challenge
Uncertainty in medical image segmentation tasks, especially inter-rater variability, arising from differences in interpretations and annotations by various experts, presents a significant challenge in achieving consisten…
Image SegmentationMedical Image SegmentationSegmentationSemantic Segmentation+1RevPHiSeg: A Memory-Efficient Neural Network for Uncertainty Quantification in Medical Image Segmentation
Quantifying segmentation uncertainty has become an important issue in medical image analysis due to the inherent ambiguity of anatomical structures and its pathologies. Recently, neural network-based uncertainty quantifi…
Efficient Neural NetworkImage SegmentationMedical Image AnalysisMedical Image Segmentation+3How disentangled are your classification uncertainties?
Uncertainty Quantification in Machine Learning has progressed to predicting the source of uncertainty in a prediction: Uncertainty from stochasticity in the data (aleatoric), or uncertainty from limitations of the model …
ClassificationDisentanglementUncertainty QuantificationSegmentation Re-thinking Uncertainty Estimation Metrics for Semantic Segmentation
In the domain of computer vision, semantic segmentation emerges as a fundamental application within machine learning, wherein individual pixels of an image are classified into distinct semantic categories. This task tran…
Decision MakingImage SegmentationSegmentationSemantic Segmentation+1