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

Tonight Distinguished Professor Denise Aberle rebooted our @RadiologyUcla research seminar series with a lecture on her research journey that led to her induction into @theNAMedicine. It was electric.

The wonderful staff at our Ackerman Blood & Platelet Center make donating easy & enjoyable. Please consider donating! Give Blood. Save Lives. Donate Today! 🩸 📱: 310.825.0888 x2 ✉️: gotblood@mednet.ucla.edu 🌐: https://t.co/ctkdcs1ifC

Thanks for having me!

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:

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… https://t.co/sKDas3ANSq

More success than I've had 😂

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 @Wenyuan1991!… https://t.co/lBQNzOBuBy

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: https://t.co/5QtM2OaXU1

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 @Wenyuan1991 and co-authors!… https://t.co/lrdJpSLOD6

New in @RadiologyUcla Proceedings: Electronic Integrated Diagnostic Report for Presenting Results of Breast Imaging and Breast Biopsy https://t.co/F9NVEPhEHO #mammo #RadQI #HITRad #FOAMrad

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.

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. https://t.co/BQhI6AhGBB

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 @ksarma! https://t.co/eDqrQXh8uL https://t.co/m6v7ZVVNXU

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 @polsonjen and @DarthDisney123! https://t.co/5QsM83qvm7

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." https://t.co/RMDrXqUMkh. Link to PDF on https://t.co/m6v7ZVVNXU. @Digi_Pathology @BillSpeier @Jiayun86541373

"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." https://t.co/2bfg5RwUrZ

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!😀

Didn’t know the Winter Olympics were uptown this year. Stay masked and be safe when playing outdoors!