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SUMMARY:Adapt or Fall Behind: Why the Best Labs Use AI
DESCRIPTION:0000Days00Hrs00Min00SecArtificial intelligence is reshaping the lab landscape—not in the future\, but now. In this webinar\, we’ll explore why adopting AI is no longer optional for modern labs and share real-world insights from clinical leaders who have already taken the leap. Learn how labs in anatomic pathology\, microbiology\, cytology\, and more are using AI to impact their staff and the patients they serve—and the lessons they’ve learned along the way. \nPresenter\n\nDr. Chen is a leader in digital and computational pathology\, holding a medical degree from the Mayo Clinic Alix School of Medicine and a master’s in Global Medicine from USC. She completed her anatomic pathology residency at Brigham and Women’s Hospital and an NIH/NCI postdoctoral fellowship in computational pathology at Harvard Medical School and Mass General Brigham. Before joining Techcyte\, Dr. Chen led digital pathology initiatives at a cancer pharmaceutical company\, driving advancements in AI-powered diagnostics.\n\n\n\nRegister Now
URL:https://www.pathologynews.com/event/adapt-or-fall-behind-why-the-best-labs-use-ai/
LOCATION:Online
CATEGORIES:PN Webinar
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SUMMARY:Spatially Resolved Transcriptomics and Graph-based Deep Learning Improve Accuracy of Routine CNS Tumor Diagnostics
DESCRIPTION:0000Days00Hrs00Min00SecRegister below! The webinar starts at 3 PM CET\, 2 PM BST\, and 9 AM EST. \nSpatially resolved transcriptomics and graph-based deep learning are transforming how researchers approach and understand central nervous system (CNS) tumors. In this webinar we will deep dive into a publication that highlights how the NePSTA (neuropathology spatial transcriptomic analysis) framework combines advanced RNA sequencing technologies with neural network analyses to uncover detailed gene expression landscapes directly within tissue samples—even in complex or ambiguous cases. By mapping gene activity across individual tumor regions\, these tools reveal previously hidden molecular characteristics\, allow for more granular tissue classification\, and address challenges posed by compromised sample quality. \nLearning Objectives:\n\nUnderstand the challenges of conventional CNS tumor diagnostics and how spatial transcriptomics addresses them.\nLearn the basics of spatially resolved transcriptomics and how graph-based deep learning is applied in neuropathology.\nCompare spatial transcriptomics with traditional molecular techniques such as DNA methylation profiling and next-generation sequencing.\n\nRegister NowSpeaker:\nProf. Felix Sahm \nProf. Dr. Dr. med. Felix Sahm\, MBA is Vice-Chair of the Department of Neuropathology and Head of Molecular Neuropathology \nUniversity of Heidelberg \nAn internationally recognized expert in brain tumor diagnostics\, he has authored over 500 publications\, including landmark studies shaping the WHO classification of CNS tumors. His research focuses on integrating molecular and multi-omic approaches to improve precision diagnostics and patient care.
URL:https://www.pathologynews.com/event/spatially-resolved-transcriptomics-and-graph-based-deep-learning-improve-accuracy-of-routine-cns-tumor-diagnostics/
LOCATION:Online
CATEGORIES:PN Webinar
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