🔥The 9th Round of Easy Loan, Earn $40 Reward is in progress❗️ ⏰ Promotion Period: January 15th - Feburary 15th, 2025 👉 Register now and check more details at gate.io/campaigns/358

Happy to report that the 3D pathology datasets from our prostate gland analysis study [W. Xie, et al. Cancer Research, 2022] are now available to all through The Cancer Imaging Archive (TCIA). #lightsheet #deeplearning #3Dpath doi.org/10.7937/44MA-G…

#uwcherryblossoms in full bloom! Time for a lab photo 🙂

Great collaboration with Andrew H. Song and Faisal Mahmood at Harvard Medical School … weakly supervised learning with #3Dpathology datasets shows that block-based analysis has advantages over 2D analysis, and that performance scales with the amount of tissue volume analyzed.

It’s one thing to publish images that represent our best “outliers”(which we all do initially), but a different challenge to image hundreds of clinical samples with near-100% yield. Here we release our tips and tricks from sample prep to QC, from years of iteration! #3Dpathology

Check out Lindsey Erion Barner 's final PhD paper from my lab! "AI-triaged 3D pathology to improve detection of esophageal neoplasia while reducing pathologist workloads" With Deepti Reddi, MD and Bill Grady at Fred Hutch Cancer Center and Faisal Mahmood at Brigham and Women's Hospital. doi.org/10.1016/j.modp…

Super excited to see our review paper on AI for computational pathology finally out!! We provide an extensive coverage of how AI has and will shape the field of pathology. Such a fun experience with my co-author Guillaume Jaume, and Faisal Mahmood Link: nature.com/articles/s4422…

Entertaining article by David Sokolov at Fred Hutch Cancer Center on our AI-triaged 3D pathology work led by Lindsey Erion Barner and Gan Gao with Deepti Reddi, MD and Bill Grady… fredhutch.org/content/www/en… via Fred Hutch Cancer Center

Our end-to-end workflow for 3D pathology is now published in Nature Protocols! This includes all the steps to go from archived pathology tissues to 3D H&E-like datasets, with an emphasis on quality control for large studies. Full text at: rdcu.be/dwIYW

#FeaturedProtocol: A workflow for robust #3Dpathology datasets of whole preclinical & clinical tissues from Kevin Bishop & Jonathan Liu bit.ly/495tDqC

HUGE NEWS! In our new preprint, we introduce “grayscale image z-stack-guided multiphoton optical-lithography” (GIZMO) to rapidly photomodulate materials in full 3D non-binary patterns at sub-µm resolutions spanning large volumes (>mm3). biorxiv.org/content/10.110… Paper 🧵 (1/19)

With #AI, it's time for pathology to go #3D sciencedirect.com/science/articl… Cell Faisal Mahmood Andrew H. Song Jonathan Liu

Exciting collaboration (and more to come) with Faisal Mahmood and Andrew H. Song , just published in Cell !

In this Cell article, Faisal Mahmood Faisal Mahmood, Andrew H. Song & Jonathan Liu develop TriPath, a method for analyzing 3D pathology samples using weakly supervised AI #ASCO24 hubs.li/Q02yvKGt0

In addition to fully computational analyses of our #3Dpathology datasets, we are exploring AI-triage methods to keep pathologists in the loop. This should enable improved diagnostic accuracy (due to comprehensive sampling of whole biopsies) while reducing pathologist workloads.

Proud of this latest publication from graduating PhD student, Kevin Bishop. In this 5th-generation of OTLS microscopes from our lab, we improve axial resolution to reduce out-of-focus background in densely labeled specimens (e.g. our fluorescent anolog of H&E).

ME Professor Jonathan Liu recently received an R01 grant from NIDDK to develop computational 3D pathology methods for Barrett’s esophagus risk stratification. Learn more: me.washington.edu/news/article/2… NIH Jonathan Liu UW Medicine Brigham and Women's Hospital Fred Hutch Cancer Center UW Engineering

3D tissues can reveal more about disease than traditional 2D slices, but they are enormously complex. Find out how a new AI tool can analyze these data-rich specimens to predict outcomes: nibib.nih.gov/news-events/ne… Andrew H. Song Jonathan Liu Faisal Mahmood Mechanical Engineering at UW

Excited to work on this with Alpenglow Biosciences Faisal Mahmood Eben L Rosenthal, MD Michael Topf MD, MSCI and our talented engineers and clinicians University of Washington !

⚡🎉 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

New review out on imaging 3D cell cultures. Congratulations to Huai-Ching and Jonathan Liu on leading this excellent new review! nature.com/articles/s4159…