PathRWKV: Enabling Whole Slide Prediction with Recurrent-Transformer

Pathological diagnosis plays a critical role in clinical practice, where the whole slide images (WSIs) are widely applied. Through a two-stage paradigm, recent deep learning approaches enhance the WSI analysis with tile-level feature extracting and slide-level feature modeling. Current Transformer models achieved improvement in the efficiency and accuracy to previous multiple instance learning based approaches. However, three core limitations persist, as they do not: (1) robustly address the modeling on variable scales for different slides, (2) effectively balance model complexity and data availability, and (3) balance training efficiency and inference performance. To explicitly address them, we propose a novel model for slide modeling, PathRWKV. Via a recurrent structure, we enable the model for dynamic perceptible tiles in slide-level modeling, which novelly enables the prediction on all tiles in the inference stage. Moreover, we employ linear attention instead of conventional matrix multiplication attention to reduce model complexity and overfitting problem. Lastly, we hinge multi-task learning to enable modeling on versatile tasks simultaneously, improving training efficiency, and asynchronous structure design to draw an effective conclusion on all tiles during inference, enhancing inference performance. Experimental results suggest that PathRWKV outperforms the current state-of-the-art methods in various downstream tasks on multiple datasets. The code and datasets are publicly available.

UnPuzzle: A Unified Framework for Pathology Image Analysis

Pathology image analysis plays a pivotal role in medical diagnosis, with deep learning techniques significantly advancing diagnostic accuracy and research. While numerous studies have been conducted to address specific pathological tasks, the lack of standardization in pre-processing methods and model/database architectures complicates fair comparisons across different approaches. This highlights the need for a unified pipeline and comprehensive benchmarks to enable consistent evaluation and accelerate research progress. In this paper, we present UnPuzzle, a novel and unified framework for pathological AI research that covers a broad range of pathology tasks with benchmark results. From high-level to low-level, upstream to downstream tasks, UnPuzzle offers a modular pipeline that encompasses data pre-processing, model composition,taskconfiguration,andexperimentconduction.Specifically, it facilitates efficient benchmarking for both Whole Slide Images (WSIs) and Region of Interest (ROI) tasks. Moreover, the framework supports variouslearningparadigms,includingself-supervisedlearning,multi-task learning,andmulti-modallearning,enablingcomprehensivedevelopment of pathology AI models. Through extensive benchmarking across multiple datasets, we demonstrate the effectiveness of UnPuzzle in streamlining pathology AI research and promoting reproducibility. We envision UnPuzzle as a cornerstone for future advancements in pathology AI, providing a more accessible, transparent, and standardized approach to model evaluation. The UnPuzzle repository is publicly available at https://github.com/Puzzle-AI/UnPuzzle.