2.2k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- National Institutes of HealthUS26 papers
- National Center for Biotechnology InformationUS7 papers
- Yale UniversityUS5 papers
- National Institutes of Health Clinical CenterUS4 papers
- National Eye InstituteUS3 papers
- National University of SingaporeSG3 papers
- Oregon Health & Science UniversityUS3 papers
- Pennsylvania State UniversityUS3 papers
- Radboud University NijmegenNL3 papers
- The University of Texas Health Science Center at HoustonUS3 papers
- University of California San DiegoUS3 papers
- Carnegie Mellon UniversityUS2 papers
9 papers · 1 filter
LMOD+: A Comprehensive Multimodal Dataset and Benchmark for Developing and Evaluating Multimodal Large Language Models in Ophthalmology
Zhenyue Qin, Yang Liu, Yu Yin +13
Vision-threatening eye diseases pose a major global health burden, with timely diagnosis limited by workforce shortages and restricted access to specialized care. While multimodal…
Hidden flaws behind expert-level accuracy of multimodal GPT-4 vision in medicine
Qiao Jin, Fangyuan Chen, Yiliang Zhou +15
Recent studies indicate that Generative Pre-trained Transformer 4 with Vision (GPT-4V) outperforms human physicians in medical challenge tasks. However, these evaluations primarily…
Synthetic Sample Selection via Reinforcement Learning
Jiarong Ye, Yuan Xue, L. Rodney Long +4
Synthesizing realistic medical images provides a feasible solution to the shortage of training data in deep learning based medical image recognition systems. However, the quality c…
DeepSeeNet: A deep learning model for automated classification of patient-based age-related macular degeneration severity from color fundus photographs
Yifan Peng, Shazia Dharssi, Qingyu Chen +5
In assessing the severity of age-related macular degeneration (AMD), the Age-Related Eye Disease Study (AREDS) Simplified Severity Scale predicts the risk of progression to late AM…
Left Ventricle Segmentation and Quantification from Cardiac Cine MR Images via Multi-task Learning
Shusil Dangi, Ziv Yaniv, Cristian A. Linte
Segmentation of the left ventricle and quantification of various cardiac contractile functions is crucial for the timely diagnosis and treatment of cardiovascular diseases. Traditi…
Scalable Simple Linear Iterative Clustering (SSLIC) Using a Generic and Parallel Approach
Bradley C. Lowekamp, David T. Chen, Ziv Yaniv +1
Superpixel algorithms have proven to be a useful initial step for segmentation and subsequent processing of images, reducing computational complexity by replacing the use of expens…
TieNet: Text-Image Embedding Network for Common Thorax Disease Classification and Reporting in Chest X-rays
Xiaosong Wang, Yifan Peng, Le Lu +2
Chest X-rays are one of the most common radiological examinations in daily clinical routines. Reporting thorax diseases using chest X-rays is often an entry-level task for radiolog…
SegAN: Adversarial Network with Multi-scale Loss for Medical Image Segmentation
Yuan Xue, Tao Xu, Han Zhang +2
Inspired by classic generative adversarial networks (GAN), we propose a novel end-to-end adversarial neural network, called SegAN, for the task of medical image segmentation. Since…
Learning Shape and Texture Characteristics of CT Tree-in-Bud Opacities for CAD Systems
Ulas Bagci, Jianhua Yao, Jesus Caban +3
Although radiologists can employ CAD systems to characterize malignancies, pulmonary fibrosis and other chronic diseases; the design of imaging techniques to quantify infectious di…