Diagnose Like A REAL Pathologist: An Uncertainty-Focused Approach for Trustworthy Multi-Resolution Multiple Instance Learning

TL;DR

This paper introduces UFC-MIL, a multi-resolution MIL approach that mimics pathologists' examination behaviors and provides well-calibrated, trustworthy diagnostic predictions using uncertainty estimation, improving model calibration without sacrificing accuracy.

Contribution

The paper proposes UFC-MIL, a novel uncertainty-focused multi-resolution MIL method that enhances calibration and interpretability in histopathological diagnosis.

Findings

UFC-MIL achieves superior calibration on public datasets.

UFC-MIL maintains classification accuracy comparable to state-of-the-art methods.

The approach mimics pathologists' examination behaviors effectively.

Abstract

With the increasing demand for histopathological specimen examination and diagnostic reporting, Multiple Instance Learning (MIL) has received heightened research focus as a viable solution for AI-centric diagnostic aid. Recently, to improve its performance and make it work more like a pathologist, several MIL approaches based on the use of multiple-resolution images have been proposed, delivering often higher performance than those that use single-resolution images. Despite impressive recent developments of multiple-resolution MIL, previous approaches only focus on improving performance, thereby lacking research on well-calibrated MIL that clinical experts can rely on for trustworthy diagnostic results. In this study, we propose Uncertainty-Focused Calibrated MIL (UFC-MIL), which more closely mimics the pathologists' examination behaviors while providing calibrated diagnostic predictions, using multiple images with different resolutions. UFC-MIL includes a novel patch-wise loss that learns the latent patterns of instances and expresses their uncertainty for classification. Also, the attention-based architecture with a neighbor patch aggregation module collects features for the classifier. In addition, aggregated predictions are calibrated through patch-level uncertainty without requiring multiple iterative inferences, which is a key practical advantage. Against challenging public datasets, UFC-MIL shows superior performance in model calibration while achieving classification accuracy comparable to that of state-of-the-art methods.