Priority-Aware Clinical Pathology Hierarchy Training for Multiple Instance Learning

TL;DR

This paper introduces a priority-aware hierarchical training method for Multiple Instance Learning in clinical pathology, improving diagnosis accuracy by emphasizing more critical symptoms and classes.

Contribution

It proposes a novel hierarchical approach that incorporates priority issues into MIL, addressing clinical needs for more accurate and symptom-aware diagnoses.

Findings

Reduces misdiagnosis in clinical pathology cases

Prioritizes more serious symptoms effectively

Enhances MIL prediction accuracy in multiclass scenarios

Abstract

Multiple Instance Learning (MIL) is increasingly being used as a support tool within clinical settings for pathological diagnosis decisions, achieving high performance and removing the annotation burden. However, existing approaches for clinical MIL tasks have not adequately addressed the priority issues that exist in relation to pathological symptoms and diagnostic classes, causing MIL models to ignore priority among classes. To overcome this clinical limitation of MIL, we propose a new method that addresses priority issues using two hierarchies: vertical inter-hierarchy and horizontal intra-hierarchy. The proposed method aligns MIL predictions across each hierarchical level and employs an implicit feature re-usability during training to facilitate clinically more serious classes within the same level. Experiments with real-world patient data show that the proposed method effectively reduces misdiagnosis and prioritizes more important symptoms in multiclass scenarios. Further analysis verifies the efficacy of the proposed components and qualitatively confirms the MIL predictions against challenging cases with multiple symptoms.