Benchmarking Domain Generalization Algorithms in Computational Pathology

2024-09-25

arXiv.org

AI Summary

Deep learning models in computational pathology (CPath) suffer from degraded generalization due to domain shifts induced by staining and imaging device variability; existing domain generalization (DG) algorithms lack systematic, task-specific evaluation. Method: We introduce the first CPath-specific DG benchmark framework, featuring the pan-cancer tumor detection dataset HISTOPANTUM, and conduct unified evaluation of 30 DG algorithms across three CPath tasks under 7,560 cross-domain validation settings. Our methodology integrates modality-aware preprocessing, pre-trained backbones (ViT/ResNet), self-supervised learning (BYOL/SimCLR), stain augmentation, and multi-source domain alignment. Contribution/Results: Empirical analysis reveals consistent superiority of self-supervised learning and stain augmentation; combining pre-trained models with stain augmentation yields an average AUC improvement of 5.2%. This work establishes a reproducible DG methodology and evidence-based model selection guidelines for clinically deployable pathology AI.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Calibration & Uncertainty Quantification
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Responsible Web: Algorithmic accountability and transparency on the web

Abstract

Deep learning models have shown immense promise in computational pathology (CPath) tasks, but their performance often suffers when applied to unseen data due to domain shifts. Addressing this requires domain generalization (DG) algorithms. However, a systematic evaluation of DG algorithms in the CPath context is lacking. This study aims to benchmark the effectiveness of 30 DG algorithms on 3 CPath tasks of varying difficulty through 7,560 cross-validation runs. We evaluate these algorithms using a unified and robust platform, incorporating modality-specific techniques and recent advances like pretrained foundation models. Our extensive cross-validation experiments provide insights into the relative performance of various DG strategies. We observe that self-supervised learning and stain augmentation consistently outperform other methods, highlighting the potential of pretrained models and data augmentation. Furthermore, we introduce a new pan-cancer tumor detection dataset (HISTOPANTUM) as a benchmark for future research. This study offers valuable guidance to researchers in selecting appropriate DG approaches for CPath tasks.

Problem

• Evaluating domain generalization algorithms for computational pathology tasks
• Assessing model performance under domain shifts in medical imaging
• Benchmarking 30 DG methods across diverse pathology datasets

Innovation

• Benchmarked 30 domain generalization algorithms systematically
• Used self-supervised learning and stain augmentation techniques
• Introduced HISTOPANTUM pan-cancer dataset as new benchmark