Learning Interpretable Microscopic Features of Tumor by Multi-task Adversarial CNNs To Improve Generalization

TL;DR

This paper introduces a multi-task adversarial CNN architecture that enhances generalization and interpretability in tumor detection from tissue images by focusing on relevant pathology features and discarding misleading ones.

Contribution

The study presents a novel end-to-end multi-task adversarial training method that improves tumor detection accuracy and interpretability in histopathology images.

Findings

Achieved an average AUC of 0.89 on breast lymph node tissue detection.

Demonstrated the model's focus on pathology features like nuclei density.

Enhanced transparency through interpretability techniques.

Abstract

Adopting Convolutional Neural Networks (CNNs) in the daily routine of primary diagnosis requires not only near-perfect precision, but also a sufficient degree of generalization to data acquisition shifts and transparency. Existing CNN models act as black boxes, not ensuring to the physicians that important diagnostic features are used by the model. Building on top of successfully existing techniques such as multi-task learning, domain adversarial training and concept-based interpretability, this paper addresses the challenge of introducing diagnostic factors in the training objectives. Here we show that our architecture, by learning end-to-end an uncertainty-based weighting combination of multi-task and adversarial losses, is encouraged to focus on pathology features such as density and pleomorphism of nuclei, e.g. variations in size and appearance, while discarding misleading features such as staining differences. Our results on breast lymph node tissue show significantly improved generalization in the detection of tumorous tissue, with best average AUC 0.89 (0.01) against the baseline AUC 0.86 (0.005). By applying the interpretability technique of linearly probing intermediate representations, we also demonstrate that interpretable pathology features such as nuclei density are learned by the proposed CNN architecture, confirming the increased transparency of this model. This result is a starting point towards building interpretable multi-task architectures that are robust to data heterogeneity. Our code is available at https://github.com/maragraziani/multitask_adversarial