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Didn’t know the Winter Olympics were uptown this year. Stay masked and be safe when playing outdoors! x.com/nightnurse38/s…

Our new article with collaborators from NIH, SUNY Upstate Medical University, and NVIDIA demonstrating the potential of federated learning to leverage multi-institutional datasets is online. We're looking for more collaboration sites!😀 academic.oup.com/jamia/advance-…

"The federated learning model exhibited superior performance and generalizability to models trained at single institutions; overall performance was significantly better than any of the institutional models alone when evaluated on held-out test sets." academic.oup.com/jamia/advance-…

Our multi-resolution model for histology analysis is now online. "The model obtained an AUROC of 99.4% and an AP of 99.8% for cancer detection on an external dataset." doi.org/10.1016/j.comp…. Link to PDF on cdx.seas.ucla.edu/publications. DigitalPathology @BillSpeier @Jiayun86541373

Many acute stroke patients arrive in the ER with with no information on when the stroke began, limiting treatment options. We developed a new #AI method for classifying stroke onset time using medical images. Nice work Jennifer Polson and Harry Zhang! sciencedirect.com/science/articl…

After analyzing 16k prostate biopsy cores, we found that 95% of cancer is detected within 10mm of the MR target! Results suggest that cancer detection rates can be maintained while taking fewer cores. Nice job Alex Raman Karthik V. Sarma, MD PhD! auajournals.org/doi/10.1097/JU… cdx.seas.ucla.edu/publications

Federated learning with patient data is poised to accelerate healthcare innovation. It's a great opportunity to involve patients and allow them to receive some kind of benefit from sharing their data. healthtechmagazine.net/article/2021/0…

In a cohort of >40k patients, our #ai EHR-based algorithm achieved a PRAUC of 0.76 for predicting depression three months out. It's a challenging problem to tackle retrospectively, but there's clearly signal that can be used to focus screening. lnkd.in/eZebnyp

New in UCLA Radiology Proceedings: Electronic Integrated Diagnostic Report for Presenting Results of Breast Imaging and Breast Biopsy escholarship.org/uc/item/1rt718… #mammo #RadQI #HITRad #FOAMrad

Our new paper in Medical Image Analysis describes a conditional generative adversarial network (GAN) that can be used to improve histology image segmentation for digital pathology applications. Nice work Wenyuan and co-authors!…lnkd.in/epqWev8q

Tumor board preparation requires a lot of time from a lot of people. We built a tool that makes the process easier and recently demonstrated a 45% time savings for pathologists covering our musculoskeletal TB. Preprint: lnkd.in/gFSw-mUJ lnkd.in/gJaAb6kF

Our new paper on active learning in digital pathology describes a framework for training #AI with less labeled data by automatically learning to distinguish between easy samples, hard samples, and noisy samples. Nice work Wenyuan!…lnkd.in/g8uuUhGx

More success than I've had 😂

Synthesizing contrast agents using #AI is a problem that I'm not sure we'll ever be able to demonstrate works convincingly, but that doesn't stop us from trying! Here's our recent paper in IEEE BME showing state-of-the-art results in brain cancer patients…lnkd.in/g_hDswQw

Grateful and honored that I could be a part of this amazing panel of speakers (still starstruck), even if I had to be there virtually. @FredNatLab Hood College. Talk posted here: youtu.be/hkKjycrN_ns

Thanks for having me!

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Tonight Distinguished Professor Denise Aberle rebooted our UCLA Radiology research seminar series with a lecture on her research journey that led to her induction into National Academy of Medicine. It was electric.

Thick-slice CT scans are common but may limit diagnostic accuracy due to lower spatial resolution. This study developed an AI model to convert thick-slice CT into synthetic thin-slice CT, showing comparable quality to real thin-slice CT & improving diagnostic accuracy for