Jonathan Liu
@jonliu123
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@UW Professor, Director of Molecular #Biophotonics Lab. Open-top #lightsheet, #invivomicroscopy, #3Dpathology, Co-founder https://t.co/n1iZm1UuDu
Seattle, WA
Joined November 2013
Excited to share our vision for nondestructive 3D pathology for clinical decision support! We discuss optical, computational and translational challenges. @adam_k_glaser @hopelessk @RederredeR @UWMadisonLOCI @anantm
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RT @AI4Pathology: ⚡🎉 We are thrilled to introduce VORTEX, an AI-powered computational framework for predicting 3D Spatial Transcriptomics (….
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RT @adam_k_glaser: New review out on imaging 3D cell cultures. Congratulations to Huai-Ching and @jonliu123 on leading this excellent new r….
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Excited to work on this with @alpenglowbio @AI4Pathology @EbenRosenthal @TopfHNS and our talented engineers and clinicians @UW !.
A new project led by ME Professor @jonliu123 aims to help surgeons to remove tumors more completely & rapidly during a single procedure. The multi-university and industry partnership is funded by an up to $21.1 million award from @ARPA_H.
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RT @NIBIBgov: 3D tissues can reveal more about disease than traditional 2D slices, but they are enormously complex. Find out how a new AI t….
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Proud of this latest publication from graduating PhD student, @Kevin_W_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).
Happy to share our latest OTLS microscope for 3D pathology from @jonliu123's lab, out now in Optics Letters!. Full text:
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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.
Our paper is out @CVPR CVMI workshop! . We report a DL approach that leverages contextual information along the depth axis to identify the highest-risk 2D sections within whole biopsies to help pathologists diagnose diseases more accurately.
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RT @CellPressNews: In this @CellCellPress article, @AI4Pathology Faisal Mahmood, @GreatAndrew90 & @jonliu123 develop TriPath, a method for….
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RT @EricTopol: With #AI, it's time for pathology to go #3D.@CellCellPress @AI4Pathology @GreatAndrew90 @jonliu123 h….
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Exciting collaboration (and more to come) with @AI4Pathology and @GreatAndrew90 , just published in Cell !.
⚡️📣👇Tremendously excited to share our new @CellCellPress 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
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RT @NatureProtocols: #FeaturedProtocol: A workflow for robust #3Dpathology datasets of whole preclinical & clinical tissues from @Kevin_W_B….
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RT @coledeforest: HUGE NEWS! In our new preprint, we introduce “grayscale image z-stack-guided multiphoton optical-lithography” (GIZMO) to….
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RT @Kevin_W_Bishop: Our end-to-end workflow for 3D pathology is now published in @NatureProtocols!. This includes all the steps to go from….
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Entertaining article by David Sokolov at Fred Hutch Cancer Center on our AI-triaged 3D pathology work led by @LindseyBarner and Gan Gao with @DeeptiReddiMD and Bill Grady…. via @fredhutch.
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RT @GreatAndrew90: Super excited to see our review paper on AI for computational pathology finally out!! We provide an extensive coverage o….
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Check out @LindseyBarner 's final PhD paper from my lab! "AI-triaged 3D pathology to improve detection of esophageal neoplasia while reducing pathologist workloads" With @DeeptiReddiMD and Bill Grady at @fredhutch and @AI4Pathology at @BrighamWomens.
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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.
Excited to share our end-to-end workflow for 3D pathology - protocol preprint is out now! More👇👇.
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Great collaboration with @GreatAndrew90 and @AI4Pathology at @harvardmed … 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.
Excited to share MAMBA, a deep learning computational platform for 3D pathology analysis, validated on microcomputed tomography and open-top light-sheet microscopy 3D datasets! #3dpathology #computationalpathology. Pre-print:
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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
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