mlsanityData to 14 Oct 2026

PLUTO-4: Frontier Pathology Foundation Models

arXiv 2511.02826 ↗First posted 4 Nov 2025cs.CVcited by 2

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Abstract (arXiv)

Foundation models trained on large-scale pathology image corpora have demonstrated strong transfer capabilities across diverse histopathology tasks. Building on this progress, we introduce PLUTO-4, our next generation of pathology foundation models that extend the Pathology-Universal Transformer (PLUTO) to frontier scale. We share two complementary Vision Transformer architectures in the PLUTO-4 family: a compact and efficient PLUTO-4S model optimized for multi-scale deployment using a FlexiViT setup with 2D-RoPE embeddings, and a frontier-scale PLUTO-4G model trained with a single patch size to maximize representation capacity and stability. Both models are pretrained using a self-supervised objective derived from DINOv2 on a large multi-institutional corpus containing 551,164 WSIs from 137,144 patients across over 50 institutions, spanning over 60 disease types and over 100 stains. Comprehensive evaluation across public and internal benchmarks demonstrates that PLUTO-4 achieves state-of-the-art performance on tasks requiring varying spatial and biological context, including tile classification, segmentation, and slide-level diagnosis. The compact PLUTO-4S provides high-throughput and robust performance for practical deployment, while PLUTO-4G establishes new performance frontiers across multiple pathology benchmarks, including an 11% improvement in dermatopathology diagnosis. These diverse improvements underscore PLUTO-4's potential to transform real-world applications as a backbone for translational research and diagnostic use cases.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 11 of its 18 references that are in the map. First posted on arXiv 4 Nov 2025.

Builds on

11 of 18 references.

References · 11

  1. 2023DINOv2: Learning Robust Visual Features without Supervisioncited by 7,974
  2. 2021Emerging Properties in Self-Supervised Vision Transformerscited by 6,968
  3. 2021RoFormer: Enhanced Transformer with Rotary Position Embeddingcited by 4,703
  4. 2024Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathologycited by 161
  5. 2022FlexiViT: One Model for All Patch Sizescited by 152
  6. 2024HEST-1k: A Dataset for Spatial Transcriptomics and Histology Image Analysiscited by 107
  7. 2019Automated Gleason Grading of Prostate Biopsies using Deep Learningcited by 69
  8. 2025Defeating the Training-Inference Mismatch via FP16cited by 66
  9. 2024RudolfV: A Foundation Model by Pathologists for Pathologistscited by 41
  10. 2024PLUTO: Pathology-Universal Transformercited by 17
  11. 2025Atlas: A Novel Pathology Foundation Model by Mayo Clinic, Charit'e, and Aignosticscited by 11

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Citing papers per year: 2026: 2.

2 papers.