From Modern CNNs to Vision Transformers: Assessing the Performance, Robustness, and Classification Strategies of Deep Learning Models in Histopathology

TL;DR

This study comprehensively evaluates modern CNNs and vision transformers in histopathology, assessing accuracy, robustness, and interpretability across multiple datasets and introducing new methodologies for model analysis.

Contribution

It introduces a new evaluation framework for deep learning models in histopathology, including robustness testing and interpretability extension for various architectures.

Findings

Vision transformers perform competitively with CNNs in histopathology classification.

Robustness to stain variations varies significantly across models.

Extended interpretability methods reveal diverse classification strategies.

Abstract

While machine learning is currently transforming the field of histopathology, the domain lacks a comprehensive evaluation of state-of-the-art models based on essential but complementary quality requirements beyond a mere classification accuracy. In order to fill this gap, we developed a new methodology to extensively evaluate a wide range of classification models, including recent vision transformers, and convolutional neural networks such as: ConvNeXt, ResNet (BiT), Inception, ViT and Swin transformer, with and without supervised or self-supervised pretraining. We thoroughly tested the models on five widely used histopathology datasets containing whole slide images of breast, gastric, and colorectal cancer and developed a novel approach using an image-to-image translation model to assess the robustness of a cancer classification model against stain variations. Further, we extended existing interpretability methods to previously unstudied models and systematically reveal insights of the models' classifications strategies that can be transferred to future model architectures.