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In a series of clinically relevant tasks in #ComputationalPathology, AI-driven models display performance #disparities across ethnic groups, which self-supervision on large training datasets and existing debiasing techniques can only partially mitigate. nature.com/articles/s4159…

I can only recommend this to anyone with a computational background interested in medical imaging and pathology. @DFKW_MD is an amazing mentor with creative and novel ideas worth exploring!

A new view on the context-resolution trade-off in pathology! We can now image entire 3D tissue blocks at high-resolution. By mitigating sampling bias, AI tools can learn from these 3D images to provide better patient prognostication! Many congrats to the amazing Andrew H. Song!

⚡️🔬📣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

Many congrats Richard J. Chen!

Mahmood Lab will be presenting three articles (TANGLE - oral, Panther, and SurvPath) at the #CVPR2025 #CVPR2024 main conference, two demos (PathChat and TriPath) and talks at the workshop on foundation models for medical vision, and computer vision for science. We are also recruiting

🚀We’re thrilled to come out of stealth and announce PathChat 2, the 1st multimodal generative AI copilot for pathology. PathChat 2 improves upon PathChat 1 (recently published nature, bit.ly/3XtFSux). youtu.be/fWDU5P0ap28 Waitlist: modella.ai 🧵1/2

Can pathology image + gene expression improve whole slide-level representation learning? Learn more about our work on TANGLE at our superstar postdoc Guillaume Jaume's oral talk at #CVPR2025 #CVPR2024 Oral Session: Orals 3C Medical and Physics-based vision Time: Thu, 20 Jun, 9:54 -

Big day for the Mahmood Lab at #CVPR2025 #CVPR2024 thanks for all the interest. Andrew H. Song Guillaume Jaume Max Lu Richard J. Chen Tong Ding Anurag Vaidya

What would you do with 1000+ spatial transcriptomics samples with corresponding H&E-stained whole-slide images? Meet HEST-1k, a collection of 1,108 ST samples assembled from 131 public and internal cohorts encompassing 25 organs, 2 species. HEST-1k includes over 1.5 million

⚡️📣Delighted to announce MMP, a prototype-based multimodal framework combining histology and transcriptomics for cancer outcome prediction, to appear in #ICML 2024 ICML Conference. Congratulations to our superstar postdoc Andrew H. Song and rest of the team who helped the study.

⚡️🔬📣 Here are our two latest preprints on how AI for Pathology can advance pre-clinical drug safety and toxicity assessment. Work led by our superstar postdoc Guillaume Jaume: Deep Learning-based Modeling for Preclinical Drug Safety Assessment 📄 Preprint:

⚡️🔬📣 We are excited to announce our new #ECCV 2024 European Conference on Computer Vision #ECCV2026 paper "Multistain Pretraining for Slide Representation Learning in Pathology" Led by Guillaume Jaume & Anurag Vaidya this work is the latest iteration of our efforts on whole slide representation learning for

🚨🔬 Super thrilled to announce a fun project with Guillaume Jaume! Using 7,000+ slides and 20+ tasks, we show that aligning H&E and immunohistochemistry slides results in robust gigapixel slide encoders.

#Pathology and laboratory #medicine is experiencing a convergence from the advancements in #digitalpathology, #computationalpathology, and #AI. Published in Nature Reviews Bioengineering 1 year ago, this scoping review provides a fundamental introduction to both historical and

⚡️📣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

Wrapping up #NeurIPS2024 w/ Andrew H. Song Max Lu Guillaume Jaume

✨📣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

Check out our two new packages for computational pathology! ⬇️ 🔱 Trident: Whole-slide image processing made easy. Support for 15+ foundation models + many helpers to simplify batch processing. 📈 Patho-Bench: Benchmark with 42 clinically-relevant tasks in oncology!

⚡🎉 We are thrilled to introduce VORTEX, an AI-powered computational framework for predicting 3D Spatial Transcriptomics (ST) using 3D tissue images and minimal 2D ST! 🧬 By combining cutting-edge 3D non-destructive tissue imaging with AI, VORTEX imputes the 3D molecular