Enhancing Reliability in Medical Image Classification of Imperfect Views

The fusion of multi-view medical images through deep neural networks is essential for boosting diagnostic precision in the field of medical image analysis. However, the reliability of these diagnostic results is often compromised by imperfections in image views, manifested as noise, artifacts, and data deficits arising from inconsistent diagnostic frequencies. These issues introduce a significant risk when merging medical views in a clinical setting. To address these problems, we introduce the Reliability-Enhanced Multi-view Network (REMNet), a novel framework designed to tackle two critical challenges: 1) reducing misclassification and uncertainty from imperfect view integration, and 2) improving the reliability and interpretability of multi-view medical image predictions. Specifically, REMNet merges information from multiple views into a coherent evidence framework and incorporates a Dirichlet prior within our predictive model to more accurately estimate confidence in predictions. Coupled with a robust fusion strategy and a precise confidence calibration process, REMNet consolidates the diverse strengths of various medical imaging views, reduces the impact of view imperfections, and enhances the reliability of medical imaging diagnostics. The superiority of REMNet is validated through comprehensive theoretical analysis and empirical experiments on multi-view medical image datasets across different modalities.