Unveiling pathology-related predictive uncertainty of glomerular lesion recognition using prototype learning.

OBJECTIVE

Recognizing glomerular lesions is essential in diagnosing chronic kidney disease. However, deep learning faces challenges due to the lesion heterogeneity, superposition, progression, and tissue incompleteness, leading to uncertainty in model predictions. Therefore, it is crucial to analyze pathology-related predictive uncertainty in glomerular lesion recognition and unveil its relationship with pathological properties and its impact on model performance.

METHODS

This paper presents a novel framework for pathology-related predictive uncertainty analysis towards glomerular lesion recognition, including prototype learning based predictive uncertainty estimation, pathology-characterized correlation analysis and weight-redistributed prediction rectification. The prototype learning based predictive uncertainty estimation includes deep prototyping, affinity embedding, and multi-dimensional uncertainty fusion. The pathology-characterized correlation analysis is the first to use expert-based and learning- based approach to construct the pathology-related characterization of lesions and tissues. The weight-redistributed prediction rectification module performs reweighting- based lesion recognition.

RESULTS

To validate the performance, extensive experiments were conducted. Based on the Spearman and Pearson correlation analysis, the proposed framework enables more efficient correlation analysis, and strong correlation with pathology-related characterization can be achieved (c index > 0.6 and p < 0.01). Furthermore, the prediction rectification module demonstrated improved lesion recognition performance across most metrics, with enhancements of up to 6.36 %.

CONCLUSION

The proposed predictive uncertainty analysis in glomerular lesion recognition offers a valuable approach for assessing computational pathology's predictive uncertainty from a pathology-related perspective.

SIGNIFICANCE: The paper provides a solution for pathology-related predictive uncertainty estimation in algorithm development and clinical practice.