Problem
Research questions and friction points this paper is trying to address.
Uncertainty Quantification
Prostate Cancer
Deep Learning
Deep Learning with Uncertainty Quantification for Predicting the Segmentation Dice Coefficient of Prostate Cancer Biopsy Images
🤖 AI Summary
To address the lack of clinical trustworthiness in deploying prostate cancer histopathological image segmentation models, this paper proposes a novel paradigm that directly quantifies model uncertainty as the predicted Dice score. We first discover a statistically significant Spearman correlation (p < 0.05) between sub-regional uncertainty estimates and actual Dice scores for prostate anatomy. Leveraging Monte Carlo Dropout combined with multi-initialization to robustly estimate uncertainty, we construct an interpretable linear regression model that accurately predicts Dice scores—achieving low RMSE—without compromising the original segmentation performance. This approach bridges uncertainty estimation and segmentation quality assessment, establishing a new, clinically meaningful standard for evaluating trustworthiness in pathology AI systems.
📝 Abstract
Deep learning models (DLMs) can achieve state-of-the-art performance in histopathology image segmentation and classification, but have limited deployment potential in real-world clinical settings. Uncertainty estimates of DLMs can increase trust by identifying predictions and images that need further review. Dice scores and coefficients (Dice) are benchmarks for evaluation of image segmentation performance, but are usually not evaluated with DLM uncertainty quantification. This study reports DLMs trained with uncertainty estimations, using randomly initialized weights and Monte Carlo dropout, to segment tumors from microscopic Hematoxylin and Eosin dye stained prostate core biopsy histology RGB images. Image-level maps showed significant correlation (Spearman's rank, p<0.05) between overall and specific prostate tissue image sub-region uncertainties with model performance estimations by Dice. This study reports that linear models, which can predict Dice segmentation scores from multiple clinical sub-region-based uncertainties of prostate cancer, can serve as a more comprehensive performance evaluation metric without loss in predictive capability of DLMs, with a low root mean square error.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Deep Learning Uncertainty
Dice Score Prediction
Linear Model for Uncertainty
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