PLUTO-4: Frontier Pathology Foundation Models
To address the weak multi-task transferability and poor generalizability of existing histopathological image models, this work introduces the PLUTO-4 family of foundation models for pathology. Methodologically, we propose a dual-branch Vision Transformer architecture—comprising the lightweight PLUTO-4S and high-performance PLUTO-4G—incorporating FlexiViT for dynamic resolution adaptation and 2D Rotary Position Embedding (2D-RoPE). Leveraging the DINOv2 framework, we perform self-supervised pretraining on 550,000 multi-center whole-slide images. Our approach enables flexible scale inference from single-resolution training and significantly enhances robustness across institutions, disease types, and staining protocols. Experiments demonstrate that PLUTO-4G achieves an 11% accuracy gain in skin pathology diagnosis, while PLUTO-4S balances high throughput with stability. Both variants attain state-of-the-art performance across diverse benchmarks—including tile-level classification, segmentation, and slide-level diagnosis—thereby strongly supporting clinical translation.