The reviewed record of science sign in
Pith

arxiv: 2501.05409 · v2 · pith:CW554276 · submitted 2025-01-09 · cs.CV · cs.AI· cs.LG

Atlas: A Novel Pathology Foundation Model by Mayo Clinic, Charit\'e, and Aignostics

Reviewed by Pithpith:CW554276open to challenge →

classification cs.CV cs.AIcs.LG
keywords modelatlasfoundationacrosscharitclinicdatasetmayo
0
0 comments X
read the original abstract

Recent advances in digital pathology have demonstrated the effectiveness of foundation models across diverse applications. In this report, we present Atlas, a novel vision foundation model based on the RudolfV approach. Our model was trained on a dataset comprising 1.2 million histopathology whole slide images, collected from two medical institutions: Mayo Clinic and Charit\'e - Universt\"atsmedizin Berlin. Comprehensive evaluations show that Atlas achieves state-of-the-art performance across twenty-one public benchmark datasets, even though it is neither the largest model by parameter count nor by training dataset size.

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Atlas H&E-TME: Scalable AI-Based Tissue Profiling at Expert Pathologist-Level Accuracy

    cs.CV 2026-06 unverdicted novelty 5.0

    Atlas H&E-TME is a new AI system for cell-level tissue profiling on H&E slides that matches pathologist performance when validated against an IHC-informed consensus and a large multi-cancer H&E annotation set.

  2. OpenTME: An Open Dataset of AI-powered H&E Tumor Microenvironment Profiles from TCGA

    cs.CV 2026-04 unverdicted novelty 4.0

    OpenTME provides pre-computed TME profiles with over 4,500 quantitative readouts per slide from 3,634 TCGA H&E images using an AI pipeline based on pathology foundation models.