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Leveraging whole slide difficulty in Multiple Instance Learning to improve prostate cancer grading

Marie Arrivat, Rémy Peyret, Elsa Angelini, Pietro Gori

TL;DR

This paper proposes two different methods to leverage WSD, a multi-task approach and a weighted classification loss approach, and applies them to Gleason grading of prostate cancer slides and shows that integrating WSD during training consistently improves the classification performance across different feature encoders and MIL methods.

Abstract

Multiple Instance Learning (MIL) has been widely applied in histopathology to classify Whole Slide Images (WSIs) with slide-level diagnoses. While the ground truth is established by expert pathologists, the slides can be difficult to diagnose for non-experts and lead to disagreements between the annotators. In this paper, we introduce the notion of Whole Slide Difficulty (WSD), based on the disagreement between an expert and a non-expert pathologist. We propose two different methods to leverage WSD, a multi-task approach and a weighted classification loss approach, and we apply them to Gleason grading of prostate cancer slides. Results show that integrating WSD during training consistently improves the classification performance across different feature encoders and MIL methods, particularly for higher Gleason grades (i.e. worse diagnosis).

Leveraging whole slide difficulty in Multiple Instance Learning to improve prostate cancer grading

TL;DR

This paper proposes two different methods to leverage WSD, a multi-task approach and a weighted classification loss approach, and applies them to Gleason grading of prostate cancer slides and shows that integrating WSD during training consistently improves the classification performance across different feature encoders and MIL methods.

Abstract

Multiple Instance Learning (MIL) has been widely applied in histopathology to classify Whole Slide Images (WSIs) with slide-level diagnoses. While the ground truth is established by expert pathologists, the slides can be difficult to diagnose for non-experts and lead to disagreements between the annotators. In this paper, we introduce the notion of Whole Slide Difficulty (WSD), based on the disagreement between an expert and a non-expert pathologist. We propose two different methods to leverage WSD, a multi-task approach and a weighted classification loss approach, and we apply them to Gleason grading of prostate cancer slides. Results show that integrating WSD during training consistently improves the classification performance across different feature encoders and MIL methods, particularly for higher Gleason grades (i.e. worse diagnosis).
Paper Structure (5 sections, 3 equations, 1 figure, 5 tables)

This paper contains 5 sections, 3 equations, 1 figure, 5 tables.

Table of Contents

  1. Introduction
  2. Methods
  3. Results and discussion
  4. Conclusion
  5. Compliance with ethical standards

Figures (1)

  • Figure 1: Attention maps of ABMIL on CTransPath with the baseline method and with the weighted classification loss. This slide was labeled as 3+3 and had no consensus. The baseline model focuses on irrelevant patches and classifies the slide as Benign while the WSD-based model focuses on a patch containing a Gleason 3 gland (circled in black) and correctly classifies the slide as Gleason 3.