WILSON - a pathology foundation model framework for patient-level analysis and diagnostic text generation

arXiv:2609.25123 · q-bio.QM, cs.AI, cs.CV, cs.LG, eess.IV · Submitted 2026-09-20 · Read on arXiv

q-bio.QM, cs.AI, cs.CV, cs.LG, eess.IV

Submitted: 2026-09-20

Updated: 2026-09-20

Comments: 56 pages, 6 main figures, with 11 additional figures and 28 tables in the appendices

Code: https://github.com/tatonetti-lab/tcga-path-reports

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Foundation Model — 51× in this paper · explained in 3 episodes
• Accuracy — 41× in this paper · explained in 1 episode
• Embeddings — 32× in this paper · explained in 2 episodes
• Foundation Models — 28× in this paper · explained in 7 episodes
• Benchmark — 23× in this paper · explained in 2 episodes
• Adaptation — 22× in this paper · explained in 1 episode
• Fine-Tuning — 19× in this paper · explained in 5 episodes
• Aggregation — 11× in this paper · explained in 1 episode
• Alignment — 10× in this paper · explained in 1 episode
• Distillation — 9× in this paper · explained in 2 episodes
• Cosine Similarity — 8× in this paper · explained in 3 episodes
• Transformer — 8× in this paper · explained in 2 episodes
• Decode — 5× in this paper · explained in 2 episodes
• Cancer Genome Atlas — 4× in this paper
• Clustering — 4× in this paper · explained in 2 episodes
• Self-Supervised Learning — 3× in this paper · explained in 7 episodes
• Vision Transformer (ViT) — 3× in this paper · explained in 2 episodes
• Benchmarking — 2× in this paper · explained in 3 episodes
• Core — 2× in this paper · explained in 2 episodes
• Domain Adaptation — 2× in this paper · explained in 5 episodes
• Domain Shift — 2× in this paper · explained in 4 episodes
• Grounding — 2× in this paper · explained in 3 episodes
• International Conference on Machine Learning — 2× in this paper
• Provenance — 2× in this paper · explained in 3 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Verification — 2× in this paper · explained in 2 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Adaptability — 1× in this paper · explained in 1 episode
• CVF International Conference on Computer Vision — 1× in this paper
• Cancer Institute Clinical Proteomic Tumor Analysis Consortium — 1× in this paper
• Computational overhead — 1× in this paper · explained in 2 episodes
• Conference on Computer Vision and Pattern Recognition — 1× in this paper
• Contrastive Learning — 1× in this paper · explained in 5 episodes
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Information Leakage — 1× in this paper · explained in 2 episodes
• Predictive Modeling — 1× in this paper · explained in 2 episodes
• Self-Supervised Learning (SSL) — 1× in this paper · explained in 4 episodes
• Self-distillation — 1× in this paper · explained in 6 episodes
• Semantic Similarity — 1× in this paper · explained in 2 episodes
• Two-Stage Architecture — 1× in this paper · explained in 2 episodes
• Unified Framework — 1× in this paper · explained in 5 episodes

Sources

• PRISM: A Multi-Modal Generative Foundation Model for Slide-Level Histopathology
• MOOZY: A Patient-First Foundation Model for Computational Pathology
• Foundation Models -- A Panacea for Artificial Intelligence in Pathology?
• PathGen-1.6M: 1.6 Million Pathology Image-text Pairs Generation through Multi-agent Collaboration
• PRISM2: Unlocking Multi-Modal General Pathology AI with Clinical Dialogue
• Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology
• Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone
• CoCa: Contrastive Captioners are Image-Text Foundation Models
• Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities
• Gemini Embedding 2: A Native Multimodal Embedding Model from Gemini
• A ConvNet for the 2020s
• HISTAI: An Open-Source, Large-Scale Whole Slide Image Dataset for Computational Pathology
• Gemma 3 Technical Report
• EmbeddingGemma: Powerful and Lightweight Text Representations

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