arXiv AI
MOSAIC: Towards a Universal Latent Space for Computational Pathology Foundation Models
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv AI
Oct 27
BatMIL: Geometry-Embedding State Space Model for Whole-Slide Image Representation
arXiv:2605.05164v2 Announce Type: replace-cross Abstract: Accurate analysis of histopathological images is critical for disease diagnosis and treatment planning. Whole-slide images (WSIs), which digi...
arXiv Machine Learning
Sep 24
FFM-CP: Cross-Backbone Fusion of Vision-Language Foundation Models for Few-Shot Computational Pathology
The paper introduces FFM-CP, a framework that fuses multiple pathology vision‑language foundation models for few‑shot learning. It aligns heterogeneous representations with an Orthogonal Procrustes transformation, then uses a unified graph to refine support‑image features and class prototypes across backbones. Experiments on six histopathology datasets show that FFM‑CP outperforms the best single adapted model in 50 of 54 few‑shot comparisons.
arXiv Machine Learning
Jun 3
Spatial Transcriptomics-Guided Alignment Enhances Molecular Profiling in Pathology Foundation Model
arXiv:2606. 03644v1 Announce Type: new Abstract: Comprehensive molecular profiling is essential for modern precision oncology but remains hindered by prohibitive costs, specimen exhaustion, and protracted turnaround times.
arXiv Computer Vision
Aug 26
Graph-Supervised Hierarchical Clinical Alignment for Radiology Report Generation with Large Language Models
arXiv:2608.24121v1 Announce Type: new Abstract: Radiology report generation (RRG) has recently benefited from large language models, which substantially improve report fluency. However, clinically fa...
arXiv AI
Sep 30
Multimodal LLMs Outperform Pathology Foundation Models in Cross-Domain Histological Similarity
arXiv:2609.32876v2 Announce Type: replace-cross Abstract: State-of-the-art pathology foundation models, trained on millions of histology tiles, can fail to preserve tissue similarity when comparisons...
arXiv AI
Jun 8
DaX: Learning General Pathology Representations Across Scales
arXiv:2606. 06983v1 Announce Type: cross Abstract: Computational pathology requires visual representations that transfer across diverse clinical endpoints and remain robust to variation in magnification, staining, scanner type, slide preparation, and input resolution.