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PLUTO-4: Frontier Pathology Foundation Models
Authors:
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
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 f…
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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.
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Submitted 11 November, 2025; v1 submitted 4 November, 2025;
originally announced November 2025.
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Learning biologically relevant features in a pathology foundation model using sparse autoencoders
Authors:
Nhat Minh Le,
Ciyue Shen,
Neel Patel,
Chintan Shah,
Darpan Sanghavi,
Blake Martin,
Alfred Eng,
Daniel Shenker,
Harshith Padigela,
Raymond Biju,
Syed Ashar Javed,
Jennifer Hipp,
John Abel,
Harsha Pokkalla,
Sean Grullon,
Dinkar Juyal
Abstract:
Pathology plays an important role in disease diagnosis, treatment decision-making and drug development. Previous works on interpretability for machine learning models on pathology images have revolved around methods such as attention value visualization and deriving human-interpretable features from model heatmaps. Mechanistic interpretability is an emerging area of model interpretability that foc…
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Pathology plays an important role in disease diagnosis, treatment decision-making and drug development. Previous works on interpretability for machine learning models on pathology images have revolved around methods such as attention value visualization and deriving human-interpretable features from model heatmaps. Mechanistic interpretability is an emerging area of model interpretability that focuses on reverse-engineering neural networks. Sparse Autoencoders (SAEs) have emerged as a promising direction in terms of extracting monosemantic features from polysemantic model activations. In this work, we trained a Sparse Autoencoder on the embeddings of a pathology pretrained foundation model. We found that Sparse Autoencoder features represent interpretable and monosemantic biological concepts. In particular, individual SAE dimensions showed strong correlations with cell type counts such as plasma cells and lymphocytes. These biological representations were unique to the pathology pretrained model and were not found in a self-supervised model pretrained on natural images. We demonstrated that such biologically-grounded monosemantic representations evolved across the model's depth, and the pathology foundation model eventually gained robustness to non-biological factors such as scanner type. The emergence of biologically relevant SAE features was generalizable to an out-of-domain dataset. Our work paves the way for further exploration around interpretable feature dimensions and their utility for medical and clinical applications.
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Submitted 16 December, 2024; v1 submitted 15 July, 2024;
originally announced July 2024.
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PLUTO: Pathology-Universal Transformer
Authors:
Dinkar Juyal,
Harshith Padigela,
Chintan Shah,
Daniel Shenker,
Natalia Harguindeguy,
Yi Liu,
Blake Martin,
Yibo Zhang,
Michael Nercessian,
Miles Markey,
Isaac Finberg,
Kelsey Luu,
Daniel Borders,
Syed Ashar Javed,
Emma Krause,
Raymond Biju,
Aashish Sood,
Allen Ma,
Jackson Nyman,
John Shamshoian,
Guillaume Chhor,
Darpan Sanghavi,
Marc Thibault,
Limin Yu,
Fedaa Najdawi
, et al. (8 additional authors not shown)
Abstract:
Pathology is the study of microscopic inspection of tissue, and a pathology diagnosis is often the medical gold standard to diagnose disease. Pathology images provide a unique challenge for computer-vision-based analysis: a single pathology Whole Slide Image (WSI) is gigapixel-sized and often contains hundreds of thousands to millions of objects of interest across multiple resolutions. In this wor…
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Pathology is the study of microscopic inspection of tissue, and a pathology diagnosis is often the medical gold standard to diagnose disease. Pathology images provide a unique challenge for computer-vision-based analysis: a single pathology Whole Slide Image (WSI) is gigapixel-sized and often contains hundreds of thousands to millions of objects of interest across multiple resolutions. In this work, we propose PathoLogy Universal TransfOrmer (PLUTO): a light-weight pathology FM that is pre-trained on a diverse dataset of 195 million image tiles collected from multiple sites and extracts meaningful representations across multiple WSI scales that enable a large variety of downstream pathology tasks. In particular, we design task-specific adaptation heads that utilize PLUTO's output embeddings for tasks which span pathology scales ranging from subcellular to slide-scale, including instance segmentation, tile classification, and slide-level prediction. We compare PLUTO's performance to other state-of-the-art methods on a diverse set of external and internal benchmarks covering multiple biologically relevant tasks, tissue types, resolutions, stains, and scanners. We find that PLUTO matches or outperforms existing task-specific baselines and pathology-specific foundation models, some of which use orders-of-magnitude larger datasets and model sizes when compared to PLUTO. Our findings present a path towards a universal embedding to power pathology image analysis, and motivate further exploration around pathology foundation models in terms of data diversity, architectural improvements, sample efficiency, and practical deployability in real-world applications.
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Submitted 13 May, 2024;
originally announced May 2024.
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Rethinking Machine Learning Model Evaluation in Pathology
Authors:
Syed Ashar Javed,
Dinkar Juyal,
Zahil Shanis,
Shreya Chakraborty,
Harsha Pokkalla,
Aaditya Prakash
Abstract:
Machine Learning has been applied to pathology images in research and clinical practice with promising outcomes. However, standard ML models often lack the rigorous evaluation required for clinical decisions. Machine learning techniques for natural images are ill-equipped to deal with pathology images that are significantly large and noisy, require expensive labeling, are hard to interpret, and ar…
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Machine Learning has been applied to pathology images in research and clinical practice with promising outcomes. However, standard ML models often lack the rigorous evaluation required for clinical decisions. Machine learning techniques for natural images are ill-equipped to deal with pathology images that are significantly large and noisy, require expensive labeling, are hard to interpret, and are susceptible to spurious correlations. We propose a set of practical guidelines for ML evaluation in pathology that address the above concerns. The paper includes measures for setting up the evaluation framework, effectively dealing with variability in labels, and a recommended suite of tests to address issues related to domain shift, robustness, and confounding variables. We hope that the proposed framework will bridge the gap between ML researchers and domain experts, leading to wider adoption of ML techniques in pathology and improving patient outcomes.
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Submitted 18 April, 2022; v1 submitted 11 April, 2022;
originally announced April 2022.