no code implementations • 31 Jan 2024 • Shreya Rajagopal, Subhashis Hazarika, Sookyung Kim, Yan-ming Chiou, Jae Ho Sohn, Hari Subramonyam, Shiwali Mohan
Given the recent advancements in Generative AI models aimed at improving the healthcare system, our work investigates whether and how generative visual question answering systems can responsibly support patient information needs in the context of radiology imaging data.
Computed Tomography (CT) Generative Visual Question Answering +2
1 code implementation • 16 Jun 2023 • Victor Quach, Adam Fisch, Tal Schuster, Adam Yala, Jae Ho Sohn, Tommi S. Jaakkola, Regina Barzilay
Translating this process to conformal prediction, we calibrate a stopping rule for sampling different outputs from the LM that get added to a growing set of candidates until we are confident that the output set is sufficient.
no code implementations • 10 Sep 2019 • Justin D Krogue, Kaiyang V Cheng, Kevin M Hwang, Paul Toogood, Eric G Meinberg, Erik J Geiger, Musa Zaid, Kevin C McGill, Rina Patel, Jae Ho Sohn, Alexandra Wright, Bryan F Darger, Kevin A Padrez, Eugene Ozhinsky, Sharmila Majumdar, Valentina Pedoia
Conclusions: Our deep learning model identified and classified hip fractures with at least expert-level accuracy, and when used as an aid improved human performance, with aided resident performance approximating that of unaided fellowship-trained attendings.
no code implementations • 28 Nov 2018 • Jaewon Yang, Dookun Park, Jae Ho Sohn, Zhen Jane Wang, Grant T. Gullberg, Youngho Seo
Deep convolutional neural networks (DCNN) have demonstrated its capability to convert MR image to pseudo CT for PET attenuation correction in PET/MRI.