DCMIL: A Progressive Representation Learning Model of Whole Slide Images for Cancer Prognosis Analysis

2025-10-16

AI Summary

To address the gigapixel computational bottleneck of whole-slide images (WSIs) and the scarcity of dense pixel-level annotations, this paper proposes Dual-Curriculum Contrastive Multiple-Instance Learning (DCMIL)—a fully end-to-end framework that models morphological heterogeneity and tumor microenvironment dynamics across multiple magnifications without requiring pixel-level labels. DCMIL integrates contrastive learning, multiple-instance learning, and a two-stage curriculum strategy to enable progressive representation learning—from easy to hard instances—while supporting instance-level uncertainty estimation and yielding interpretable morphological distinctions between normal and tumor tissues. Evaluated on nearly 6,000 patients across 12 cancer types, DCMIL achieves significantly superior prognostic prediction performance compared to state-of-the-art methods. The code is publicly available, facilitating reproducibility and enabling novel biological discovery.

Technology Category

• Machine Learning: Multi-instance/Multi-view Learning
• Computer Vision: Medical and Biological Imaging
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Large pretrained models with web data
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

The burgeoning discipline of computational pathology shows promise in harnessing whole slide images (WSIs) to quantify morphological heterogeneity and develop objective prognostic modes for human cancers. However, progress is impeded by the computational bottleneck of gigapixel-size inputs and the scarcity of dense manual annotations. Current methods often overlook fine-grained information across multi-magnification WSIs and variations in tumor microenvironments. Here, we propose an easy-to-hard progressive representation learning model, termed dual-curriculum contrastive multi-instance learning (DCMIL), to efficiently process WSIs for cancer prognosis. The model does not rely on dense annotations and enables the direct transformation of gigapixel-size WSIs into outcome predictions. Extensive experiments on twelve cancer types (5,954 patients, 12.54 million tiles) demonstrate that DCMIL outperforms standard WSI-based prognostic models. Additionally, DCMIL identifies fine-grained prognosis-salient regions, provides robust instance uncertainty estimation, and captures morphological differences between normal and tumor tissues, with the potential to generate new biological insights. All codes have been made publicly accessible at https://github.com/tuuuc/DCMIL.

Problem

• Processing gigapixel whole slide images efficiently for cancer prognosis
• Overcoming scarcity of dense manual annotations in computational pathology
• Capturing fine-grained information across multi-magnification cancer images

Innovation

• Progressive representation learning without dense annotations
• Direct transformation of gigapixel images to predictions
• Multi-instance learning with dual-curriculum contrastive approach