no code implementations • 22 Mar 2024 • Yiliang Zhou, Hanley Ong, Patrick Kennedy, Carol Wu, Jacob Kazam, Keith Hentel, Adam Flanders, George Shih, Yifan Peng
The study examines the application of GPT-4V, a multi-modal large language model equipped with visual recognition, in detecting radiological findings from a set of 100 chest radiographs and suggests that GPT-4V is currently not ready for real-world diagnostic usage in interpreting chest radiographs.
no code implementations • 25 Jan 2024 • Mingquan Lin, TianHao Li, Zhaoyi Sun, Gregory Holste, Ying Ding, Fei Wang, George Shih, Yifan Peng
Our proposed AI model utilizes supervised contrastive learning to minimize bias in CXR diagnosis.
no code implementations • 24 Oct 2023 • Gregory Holste, Yiliang Zhou, Song Wang, Ajay Jaiswal, Mingquan Lin, Sherry Zhuge, Yuzhe Yang, Dongkyun Kim, Trong-Hieu Nguyen-Mau, Minh-Triet Tran, Jaehyup Jeong, Wongi Park, Jongbin Ryu, Feng Hong, Arsh Verma, Yosuke Yamagishi, Changhyun Kim, Hyeryeong Seo, Myungjoo Kang, Leo Anthony Celi, Zhiyong Lu, Ronald M. Summers, George Shih, Zhangyang Wang, Yifan Peng
Many real-world image recognition problems, such as diagnostic medical imaging exams, are "long-tailed" $\unicode{x2013}$ there are a few common findings followed by many more relatively rare conditions.
1 code implementation • 17 Aug 2023 • Gregory Holste, Ziyu Jiang, Ajay Jaiswal, Maria Hanna, Shlomo Minkowitz, Alan C. Legasto, Joanna G. Escalon, Sharon Steinberger, Mark Bittman, Thomas C. Shen, Ying Ding, Ronald M. Summers, George Shih, Yifan Peng, Zhangyang Wang
This work represents a first step toward understanding the impact of pruning on model behavior in deep long-tailed, multi-label medical image classification.
1 code implementation • 29 Aug 2022 • Gregory Holste, Song Wang, Ziyu Jiang, Thomas C. Shen, George Shih, Ronald M. Summers, Yifan Peng, Zhangyang Wang
Imaging exams, such as chest radiography, will yield a small set of common findings and a much larger set of uncommon findings.
Ranked #1 on Long-tail Learning on MIMIC-CXR-LT
1 code implementation • 19 Mar 2022 • Song Wang, Mingquan Lin, Ying Ding, George Shih, Zhiyong Lu, Yifan Peng
Analyzing radiology reports is a time-consuming and error-prone task, which raises the need for an efficient automated radiology report analysis system to alleviate the workloads of radiologists and encourage precise diagnosis.
no code implementations • 3 Feb 2022 • Timothy L. Kline, Felipe Kitamura, Ian Pan, Amine M. Korchi, Neil Tenenholtz, Linda Moy, Judy Wawira Gichoya, Igor Santos, Steven Blumer, Misha Ysabel Hwang, Kim-Ann Git, Abishek Shroff, Elad Walach, George Shih, Steve Langer
The goal of this series is to provide resources to not only help improve the review process for A. I.-based medical imaging papers, but to facilitate a standard for the information that is presented within all components of the research study.
no code implementations • 11 Jan 2022 • Song Wang, Liyan Tang, Mingquan Lin, George Shih, Ying Ding, Yifan Peng
In this work, we propose to mine and represent the associations among medical findings in an informative knowledge graph and incorporate this prior knowledge with radiology report generation to help improve the quality of generated reports.
no code implementations • 25 Nov 2020 • Yan Han, Chongyan Chen, Liyan Tang, Mingquan Lin, Ajay Jaiswal, Song Wang, Ahmed Tewfik, George Shih, Ying Ding, Yifan Peng
After a number of iterations and with the help of radiomic features, our framework can converge to more accurate image regions.
no code implementations • 7 Aug 2020 • Veronica Rotemberg, Nicholas Kurtansky, Brigid Betz-Stablein, Liam Caffery, Emmanouil Chousakos, Noel Codella, Marc Combalia, Stephen Dusza, Pascale Guitera, David Gutman, Allan Halpern, Harald Kittler, Kivanc Kose, Steve Langer, Konstantinos Lioprys, Josep Malvehy, Shenara Musthaq, Jabpani Nanda, Ofer Reiter, George Shih, Alexander Stratigos, Philipp Tschandl, Jochen Weber, H. Peter Soyer
Prior skin image datasets have not addressed patient-level information obtained from multiple skin lesions from the same patient.