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Excited to announce CONCH, a new visual language foundation model for #pathology, trained with 1.17 Million pathology image / caption pairs and achieves SOTA performance on zero-shot classification, text-to-img retrieval, segmentation and more! Pre-print: arxiv.org/abs/2307.12914

Excited to present UNI - a general-purpose self-supervised visual model for #CPath pretrained using 100M+ images across 100K+ WSIs! Co-led with Tong Ding Max Lu @DFKW_MD Faisal Mahmood Harvard Medical School Summary: bit.ly/3EzEFr0 Preprint: arxiv.org/abs/2308.15474 1/

⚡️🔬📣Excited to share our two new Nature Medicine articles, we develop computational pathology foundation models, 1. UNI, a self-supervised computational pathology model trained on 100 million pathology images from 100k+ slides. 2. CONCH, a vision-language model for

Two remarkable new papers Nature Medicine on foundation models #AI for pathology nature.com/articles/s4159… 100,000 whole slide images w/ >100 million path images nature.com/articles/s4159… Multimodal of ~1.2 million images and text pairs Faisal Mahmood Richard J. Chen Max Lu @DFKW_MD

Its been a month since we released UNI and CONCH, a vision only and a vision-language foundation model for computational pathology w/ Nature Medicine articles. The models have been downloaded over 233k times on @HuggingFace we are analyzing the early impact, see where people are

⚡️📣👇Tremendously excited to share our new Cell article, where we develop TriPath, a method for analyzing 3D pathology samples using weakly supervised AI. Article: authors.elsevier.com/a/1j3RiL7PXqQM-. TriPath enables 3D computational pathology via 3D multiple instance learning

⚡️🔬📣Thrilled and excited to share that we will be presenting three articles at #CVPR2025 2024 related to whole slide level representation learning, multimodal contrastive learning and multimodal fusion. #CVPR2024 #ComputationalPathology 1. TANGLE: Transcriptomics-guided Slide

Excited to announce (*4 months late😅) that I am #phdone! Crossing this finish line would not be possible without the support and collaboration of many colleagues and mentors, and also without Faisal Mahmood ! With Faisal Mahmood , Guillaume Jaume , Andrew H. Song,

⚡️🔬📣 Excited to share our new nature article building and evaluating PathChat, a multimodal generative AI copilot and chatbot for human pathology. Article: nature.com/articles/s4158… Open Access Link: rdcu.be/dKC0r We leverage our previous success in building

Excellent work from Gabrielle Campanella, Thomas J. Fuchs Icahn School of Medicine at Mount Sinai! Benchmarking 8 pathology foundation models including UNI, Virchow and Prov-GigaPath on large scale datasets for clinical diagnosis and biomarker discovery and providing insights into data scaling laws and

The in-print version of the PathChat nature article is now available online with open access nature.com/articles/s4158… learn more about next steps and how PathChat is further being developed at ModellaAI

⚡️📣After the success of our previous pathology foundation models UNI (rdcu.be/dBMgh) and CONCH (rdcu.be/dBMf6), we are now announcing TITAN (arxiv.org/abs/2411.19666), a new state-of-the-art whole slide level foundation model trained on >330k pathology slides

⚡️Announcing UNI 2: Over the past nine months its been humbling to see how UNI (rdcu.be/dBMgh) and CONCH (rdcu.be/dBMf6) our two foundation models for computational pathology have been downloaded >1 Million times and used in >400 studies. Today we are excited

✨📣Introducing THREADS: a multimodal foundation model for pathology trained on paired histology and genomic data 🔬+🧬 We show that: (a) THREADS achieves SOTA performance on >50 tasks in oncologic pathology with much less pre-training data than other models, highlighting the

Excited to see our work highlighted by Meta AI at Meta! Including UNI (nature.com/articles/s4159…) and PathChat (nature.com/articles/s4158…). ai.meta.com/blog/mahmood-l…

One year since releasing UNI and CONCH, our computational pathology models have 1.4M+ downloads, have been used by hundreds of studies, and unlocked unique capabilities. Update here: linkedin.com/pulse/one-year…

📣 Excited to share our new ICML 2025 Spotlight article, “Do Multiple Instance Learning Models Transfer?” – addressing a foundational question for building robust and generalizable MIL models. Read the article: arxiv.org/pdf/2506.09022 👉Enhanced Performance & Robustness: