Faisal Mahmood
@AI4Pathology • 6,976 subscribers
Associate Prof. @Harvard | Faculty @harvardmed @MassGenBrigham @broadinstitute @harvard_data | Multimodal, Generative, & Agentic AI for Biomedicine
Videos
⚡️🔬📣 Excited to share our new nature article building and evaluating PathChat, a multimodal generative AI copilot and chatbot for human pathology. Article: Open Access Link: We leverage our previous success in building foundation models for computational pathology such as UNI / CONCH and combine it with the advancements of large vision language models and generative AI to enable PathChat to answer diverse pathology-related queries. We assessed PathChat using both multiple choice diagnostic questions and open-ended questions. Congratulations to Max Lu Bowen Chen @DFKW_MD Richard J. Chen and everyone else who contributed to this work. Also see blog post from Max Lu about this work: , also teasing the development and preview of PathChat 2, a successor to PathChat 1 bringing new capabilities and substantially improved performance to the state-of-the-art.
Faisal Mahmood291,419 次观看 • 2 年前
⚡️📣👇Tremendously excited to share our new Cell article, where we develop TriPath, a method for analyzing 3D pathology samples using weakly supervised AI. Article: TriPath enables 3D computational pathology via 3D multiple instance learning allowing AI models to capture intricate morphological details from pathology volumes. Code: Blog post: Tested on two different imaging modalities, and patient cohorts from two institutions. Our superstar Andrew H. Song put in a monumental effort of leading the study, in a fantastic collaboration with Jonathan Liu at University of Washington . Interesting aspects: - Utilizing the whole tissue volume and leveraging 3D deep learning enable superior risk prediction performance compared to 2D deep learning baselines based on a few sampled tissue sections that emulate standard clinical practice. This indicates TriPath can harness additional information provided by 3D tissue morphology. - The performance is also superior to clinical baselines from a reader study that involved six expert pathologists. - The morphologically heterogeneous tissue volume could lead to opposing patient-level outcome predictions, dependent on which portion of the tissue volume is used. This concurs with current clinical literature warning that tissue sampling bias can lead to misdiagnosis. Some limitations: - While the 3D pathology cohort size is unprecedented, it is smaller than typical 2D pathology cohorts. Further large-scale studies will be required for validation. Nevertheless, we believe that this study will initiate a positive cycle, encouraging academic institutions and pharmaceutical companies to contribute large banks of human tissue blocks with paired clinical outcomes, thus speeding up advancements in 3D computational pathology. Concluding insights: We believe that 3D pathology is just around the corner - It has the huge potential to not only augment/improve the current clinical practice centered around 2D examination of human tissue, but also help reveal novel biomarkers for prognosis and therapeutic response.. Harvard Medical School Harvard Data Science Initiative Mass General Brigham Broad Institute
Faisal Mahmood65,520 次观看 • 2 年前
⚡🎉 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 landscape of large tissue samples in a cost-effective and scalable manner. 🧠💡Our approach: By pretraining on diverse 3D morphology–2D transcriptomic pairs from heterogeneous tissue samples, and then fine-tuning on minimal 2D ST data from a volume of interest, VORTEX leverages both generic tissue-specific and sample-specific morphomolecular correlates to predict 3D ST. Congratulations to our superstar co-leads Cristina Almagro Pérez and Andrew H. Song, this was an exciting collaboration with Jonathan Liu Sizun Jiang Ali Bashashati. Preprint: Demo: Read the excellent blog from our superstar grad student Cristina Almagro Pérez: Also see our previous work on 3D Computational Pathology from Andrew H. Song published in Cell last year: Stay tuned for more to come.
Faisal Mahmood17,969 次观看 • 1 年前
⚡️📣Today we are tremendously excited to announce ModellaAI the first startup from Mahmood Lab, based on an array of foundation models and generative AI tools including our recent PathChat article in nature ( ModellaAI will actualize these exciting developments and put them in the hands of pathologists, clinicians, researchers, and trainees. See our announcement below and sign up for the PathChat 2 waitlist at Congratulations to the entire team and especially Richard J. Chen Jill Stefanelli Max Lu kuanchen Bowen Chen, Long Le, and everyone else, stay tuned for exciting additional announcements.
Faisal Mahmood17,220 次观看 • 2 年前
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