PLUTO-4: Frontier Pathology Foundation Models
Harshith Padigela, Shima Nofallah, Atchuth Naveen Chilaparasetti, Ryun Han, Andrew Walker, Judy Shen, Chintan Shah, Blake Martin, Aashish Sood, Elliot Miller, Ben Glass, Andy Beck, Harsha Pokkalla, Syed Ashar Javed

TL;DR
PLUTO-4 introduces advanced pathology foundation models with two architectures, achieving state-of-the-art results across diverse histopathology tasks and demonstrating significant improvements in diagnostic accuracy.
Contribution
The paper presents PLUTO-4, a new set of pathology foundation models with scalable architectures trained on extensive multi-institutional data, pushing the limits of performance in histopathology tasks.
Findings
PLUTO-4 achieves state-of-the-art performance on multiple benchmarks.
The compact PLUTO-4S model enables high-throughput deployment.
PLUTO-4G improves dermatopathology diagnosis by 11%.
Abstract
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.…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAI in cancer detection · Cutaneous Melanoma Detection and Management · Digital Imaging for Blood Diseases
