PLUTO-4: Frontier Pathology Foundation Models
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arXiv
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| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915611631681536 |
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| author | Padigela, Harshith Nofallah, Shima Chilaparasetti, Atchuth Naveen Han, Ryun Walker, Andrew Shen, Judy Shah, Chintan Martin, Blake Sood, Aashish Miller, Elliot Glass, Ben Beck, Andy Pokkalla, Harsha Javed, Syed Ashar |
| author_facet | Padigela, Harshith Nofallah, Shima Chilaparasetti, Atchuth Naveen Han, Ryun Walker, Andrew Shen, Judy Shah, Chintan Martin, Blake Sood, Aashish Miller, Elliot Glass, Ben Beck, Andy Pokkalla, Harsha Javed, Syed Ashar |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_02826 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PLUTO-4: Frontier Pathology Foundation Models Padigela, Harshith Nofallah, Shima Chilaparasetti, Atchuth Naveen Han, Ryun Walker, Andrew Shen, Judy Shah, Chintan Martin, Blake Sood, Aashish Miller, Elliot Glass, Ben Beck, Andy Pokkalla, Harsha Javed, Syed Ashar Computer Vision and Pattern Recognition 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. |
| title | PLUTO-4: Frontier Pathology Foundation Models |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2511.02826 |