# UnPuzzle: A Unified Framework for Pathology Image Analysis

arXiv: https://arxiv.org/abs/2503.03152 · HTML: https://mlsanity.com/paper/2503.03152/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Mar 2025.

## Built on by (most cited first, 5 of 5)

- [Enhancing Pathological VLMs with Cross-scale Reasoning](https://mlsanity.com/paper/2606.17412/) (2026)
- [MambaBack: Bridging Local Features and Global Contexts in Whole Slide Image Analysis](https://mlsanity.com/paper/2604.15729/) (2026)
- [SSMamba: A Self-Supervised Hybrid State Space Model for Pathological Image Classification](https://mlsanity.com/paper/2604.15711/) (2026)
- [Geometry-Aware State Space Model: A New Paradigm for Whole-Slide Image Representation](https://mlsanity.com/paper/2605.05164/) (2026)
- [SlideMix: Enhancing Whole Slide Image Analysis via Multimodal Shuffling](https://mlsanity.com/paper/2609.00396/) (2026)

## Abstract (arXiv)

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.