# Accelerating Data Processing and Benchmarking of AI Models for Pathology

arXiv: https://arxiv.org/abs/2502.06750 · HTML: https://mlsanity.com/paper/2502.06750/

## Key facts

- Cited by 65 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Feb 2025.

## Built on by (most cited first, 30 of 65)

- [Evidence-based diagnostic reasoning with multi-agent copilot for human pathology](https://mlsanity.com/paper/2506.20964/) (2025)
- [PathAgent: Toward Interpretable Analysis of Whole-slide Pathology Images via Large Language Model-based Agentic Reasoning](https://mlsanity.com/paper/2511.17052/) (2025)
- [PixCell: A generative foundation model for digital histopathology images](https://mlsanity.com/paper/2506.05127/) (2025)
- [Foundation Models in Medical Imaging: A Review and Outlook](https://mlsanity.com/paper/2506.09095/) (2025)
- [A Large-Scale Benchmark of Cross-Modal Learning for Histology and Gene Expression in Spatial Transcriptomics](https://mlsanity.com/paper/2508.01490/) (2025)
- [TICON: A Slide-Level Tile Contextualizer for Histopathology Representation Learning](https://mlsanity.com/paper/2512.21331/) (2025)
- [Fusion of Multi-scale Heterogeneous Pathology Foundation Models for Whole Slide Image Analysis](https://mlsanity.com/paper/2510.27237/) (2025)
- [Beyond Pixel Simulation: Pathology Image Generation via Diagnostic Semantic Tokens and Prototype Control](https://mlsanity.com/paper/2512.21058/) (2025)
- [AtlasPatch: Scalable Foundation Model-based Tissue Detection and Patch Extraction for Computational Pathology](https://mlsanity.com/paper/2602.03998/) (2026)
- [MOOZY: A Patient-First Foundation Model for Computational Pathology](https://mlsanity.com/paper/2603.27048/) (2026)
- [SGPMIL: Sparse Gaussian Process Multiple Instance Learning](https://mlsanity.com/paper/2507.08711/) (2025)
- [Learning the Language of Histopathology Images reveals Prognostic Subgroups in Invasive Lung Adenocarcinoma Patients](https://mlsanity.com/paper/2508.16742/) (2025)
- [Leveraging Foundation Models for Histological Grading in Cutaneous Squamous Cell Carcinoma using PathFMTools](https://mlsanity.com/paper/2511.19751/) (2025)
- [Democratising Pathology Co-Pilots: An Open Pipeline and Dataset for Whole-Slide Vision-Language Modelling](https://mlsanity.com/paper/2512.17326/) (2025)
- [Computational Pathology in the Era of Emerging Foundation and Agentic AI -- International Expert Perspectives on Clinical Integration and Translational Readiness](https://mlsanity.com/paper/2603.05884/) (2026)
- [MVHybrid: Improving Spatial Transcriptomics Prediction with Hybrid State Space-Vision Transformer Backbone in Pathology Vision Foundation Models](https://mlsanity.com/paper/2508.00383/) (2025)
- [Morphology-Aware Prognostic model for Five-Year Survival Prediction in Colorectal Cancer from H&E Whole Slide Images](https://mlsanity.com/paper/2510.14800/) (2025)
- [MPath: Multimodal Pathology Report Generation from Whole Slide Images](https://mlsanity.com/paper/2512.11906/) (2025)
- [A Multicenter Benchmark of Multiple Instance Learning Models for Lymphoma Subtyping from HE-stained Whole Slide Images](https://mlsanity.com/paper/2512.14640/) (2025)
- [PathMoE: Interpretable Multimodal Interaction Experts for Pediatric Brain Tumor Classification](https://mlsanity.com/paper/2603.01547/) (2026)
- [Clinically-Informed Modeling for Pediatric Brain Tumor Classification from Whole-Slide Histopathology Images](https://mlsanity.com/paper/2604.21060/) (2026)
- [Semantic Context-aware mOdality fUsion Transformer (SCOUT): A Context-Aware Multimodal Transformer for Concept-Grounded Pathology Report Generation](https://mlsanity.com/paper/2605.01144/) (2026)
- [Benchmarking Pathology Foundation Models for Spatial Domain Understanding](https://mlsanity.com/paper/2605.25764/) (2026)
- [Cascade Detector Analysis and Application to Biomedical Microscopy](https://mlsanity.com/paper/2504.21598/) (2025)
- [The Butterfly Effect in Pathology: Exploring Security in Pathology Foundation Models](https://mlsanity.com/paper/2505.24141/) (2025)
- [Predicting EGFR Mutation in LUAD from Histopathological Whole-Slide Images Using Pretrained Foundation Model and Transfer Learning: An Indian Cohort Study](https://mlsanity.com/paper/2508.01352/) (2025)
- [AdaFusion: Prompt-Guided Inference with Adaptive Fusion of Pathology Foundation Models](https://mlsanity.com/paper/2508.05084/) (2025)
- [Integrating Pathology and CT Imaging for Personalized Recurrence Risk Prediction in Renal Cancer](https://mlsanity.com/paper/2508.21581/) (2025)
- [ModalSurv: Investigating opportunities and limitations of multimodal deep survival learning in prostate and bladder cancer](https://mlsanity.com/paper/2509.05037/) (2025)
- [Hierarchical Generalized Category Discovery for Brain Tumor Classification in Digital Pathology](https://mlsanity.com/paper/2510.02760/) (2025)

## Abstract (arXiv)

Advances in foundation modeling have reshaped computational pathology. However, the increasing number of available models and lack of standardized benchmarks make it increasingly complex to assess their strengths, limitations, and potential for further development. To address these challenges, we introduce a new suite of software tools for whole-slide image processing, foundation model benchmarking, and curated publicly available tasks. We anticipate that these resources will promote transparency, reproducibility, and continued progress in the field.