Richard J. Chen (@richardjchen) 's Twitter Profile
Richard J. Chen

@richardjchen

CTO @Modella_AI • PhD @harvardmed

ID: 18647972

linkhttps://scholar.google.com/citations?user=yhGqdMgAAAAJ&hl=en calendar_today05-01-2009 21:54:01

284 Tweet

999 Followers

548 Following

Max Lu (@mylu97) 's Twitter Profile Photo

With 350K+ downloads of UNI (nature.com/articles/s4159…) and CONCH (nature.com/articles/s4159…), it has been really amazing to see how quickly these models are being adopted into CPath research. Sign up to access models: huggingface.co/MahmoodLab/UNI and huggingface.co/MahmoodLab/CON…. With

With 350K+ downloads of UNI (nature.com/articles/s4159…) and CONCH (nature.com/articles/s4159…), it has been really amazing to see how quickly these models are being adopted into CPath research.

Sign up to access models: huggingface.co/MahmoodLab/UNI and huggingface.co/MahmoodLab/CON….

With
Faisal Mahmood (@ai4pathology) 's Twitter Profile Photo

⚡️🔬📣 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:

⚡️🔬📣 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 <a href="/GuillaumeJaume/">Guillaume Jaume</a>:

Deep Learning-based Modeling for Preclinical Drug Safety Assessment

📄 Preprint:
ModellaAI (@modella_ai) 's Twitter Profile Photo

We're thrilled to see PathChat highlighted in nature's tech feature on "#GPT for science"! 🎉 PathChat 2 is revolutionizing #pathology workflows with cutting-edge #multimodal #generative #agentic #AI. 🔗 Read more at: nature.com/articles/d4158… 💡 Interested in learning more or

Faisal Mahmood (@ai4pathology) 's Twitter Profile Photo

⚡️🔬📣 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

⚡️🔬📣 We are excited to announce our new #ECCV 2024 <a href="/eccvconf/">European Conference on Computer Vision #ECCV2026</a> paper "Multistain Pretraining for Slide Representation Learning in Pathology" Led by <a href="/GuillaumeJaume/">Guillaume Jaume</a> &amp; <a href="/anurag_vaidya7/">Anurag Vaidya</a> this work is the latest iteration of our efforts on whole slide representation learning for
Jakob Nikolas Kather (@jnkath) 's Twitter Profile Photo

New research from katherlab on #computational #pathology: We benchmark pathology foundation models 👇 Several of these are publicly available under permissive licenses. arxiv.org/abs/2408.15823 Some takeaways: 1. The vision-language model CONCH (Faisal Mahmood) outperforms

New research from <a href="/katherlab/">katherlab</a> on #computational #pathology: We benchmark pathology foundation models 👇 Several of these are publicly available under permissive licenses. 
arxiv.org/abs/2408.15823 
Some takeaways:
1. The vision-language model CONCH (<a href="/AI4Pathology/">Faisal Mahmood</a>)  outperforms
Faisal Mahmood (@ai4pathology) 's Twitter Profile Photo

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

The in-print version of the PathChat <a href="/Nature/">nature</a> 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 <a href="/modella_ai/">ModellaAI</a>
Richard J. Chen (@richardjchen) 's Twitter Profile Photo

#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

Richard J. Chen (@richardjchen) 's Twitter Profile Photo

🚀 Thrilled to announce the launch of Judith, our #ai #agent for biomedical image analysis at ModellaAI ⚡Judith brings the power of #genai and foundation models to clinicians, researchers, and scientists in streamlining complex workflows and accelerating discoveries across

Todd Hollon (@toddchollon) 's Twitter Profile Photo

🚀 Proud to introduce #FastGlioma: the first foundation model enabling rapid, accurate detection of brain tumor infiltration during surgery, in under 10 seconds. With FastGlioma, we’re minimizing the risk of residual tumor and enhancing outcomes for glioma patients. This work

Faisal Mahmood (@ai4pathology) 's Twitter Profile Photo

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…

Faisal Mahmood (@ai4pathology) 's Twitter Profile Photo

👉📣Introducing KRONOS, a panel-agnostic foundation model purpose-built for multiplex spatial proteomics! Access the pre-print here: arxiv.org/pdf/2506.03373 Trained self-supervised on 47 million single-marker patches spanning 175 protein markers, 16 tissue types, 8 imaging

👉📣Introducing KRONOS, a panel-agnostic foundation model purpose-built for multiplex spatial proteomics! Access the pre-print here: arxiv.org/pdf/2506.03373

Trained self-supervised on 47 million single-marker patches spanning 175 protein markers, 16 tissue types, 8 imaging
Faisal Mahmood (@ai4pathology) 's Twitter Profile Photo

📣 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:

📣 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 &amp; Robustness: