Unlocking adaptive digital pathology through dynamic feature learning

2024-12-29

AI Summary

Existing pathology foundation models suffer from static representations, limiting their clinical generalizability across diverse staining protocols and downstream tasks across hospitals. To address this, we propose PathFiT—the first dynamic feature tuning framework for digital pathology—enabling task-adaptive, plug-and-play feature projection without architectural modifications, via parameter-efficient fine-tuning (PEFT). PathFiT is compatible with multimodal histological stains—including H&E, Masson’s Trichrome, and immunofluorescence—as well as mainstream vision foundation models. Evaluated on a comprehensive benchmark comprising 35 pathology tasks and built upon 20 TB of real-world data, PathFiT achieves state-of-the-art (SOTA) performance on 34 tasks. Notably, it delivers an average 10.20% performance gain on specialized imaging tasks, substantially overcoming the generalization bottleneck imposed by static representations in multi-stain, multi-task settings.

Technology Category

• Machine Learning: Deep Neural Architectures and Foundation Models
• Computer Vision: Multi-modal Vision
• Search and Optimization: Learning to Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• 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

Abstract

Foundation models have revolutionized the paradigm of digital pathology, as they leverage general-purpose features to emulate real-world pathological practices, enabling the quantitative analysis of critical histological patterns and the dissection of cancer-specific signals. However, these static general features constrain the flexibility and pathological relevance in the ever-evolving needs of clinical applications, hindering the broad use of the current models. Here we introduce PathFiT, a dynamic feature learning method that can be effortlessly plugged into various pathology foundation models to unlock their adaptability. Meanwhile, PathFiT performs seamless implementation across diverse pathology applications regardless of downstream specificity. To validate PathFiT, we construct a digital pathology benchmark with over 20 terabytes of Internet and real-world data comprising 28 H&E-stained tasks and 7 specialized imaging tasks including Masson's Trichrome staining and immunofluorescence images. By applying PathFiT to the representative pathology foundation models, we demonstrate state-of-the-art performance on 34 out of 35 tasks, with significant improvements on 23 tasks and outperforming by 10.20% on specialized imaging tasks. The superior performance and versatility of PathFiT open up new avenues in computational pathology.

Problem

• Pathology Models
• Hospital Adaptability
• Flexibility Limitations

Innovation

• PathFiT
• Digital Pathology
• Adaptability