87 citations · 227 across the 13 of their papers we have counts for
13 papers
Extreme Cardiac MRI Analysis under Respiratory Motion: Results of the CMRxMotion Challenge
Kang Wang, Chen Qin, Zhang Shi +46
Deep learning models have achieved state-of-the-art performance in automated Cardiac Magnetic Resonance (CMR) analysis. However, the efficacy of these models is highly dependent on…
Understanding Dataset Bias in Medical Imaging: A Case Study on Chest X-rays
Ethan Dack, Chengliang Dai
Recent works have revisited the infamous task ``Name That Dataset'', demonstrating that non-medical datasets contain underlying biases and that the dataset origin task can be solve…
A Hybrid Framework with Large Language Models for Rare Disease Phenotyping
Jinge Wu, Hang Dong, Zexi Li +7
Rare diseases pose significant challenges in diagnosis and treatment due to their low prevalence and heterogeneous clinical presentations. Unstructured clinical notes contain valua…
The Extreme Cardiac MRI Analysis Challenge under Respiratory Motion (CMRxMotion)
Shuo Wang, Chen Qin, Chengyan Wang +12
The quality of cardiac magnetic resonance (CMR) imaging is susceptible to respiratory motion artifacts. The model robustness of automated segmentation techniques in face of real-wo…
Suggestive Annotation of Brain MR Images with Gradient-guided Sampling
Chengliang Dai, Shuo Wang, Yuanhan Mo +3
Machine learning has been widely adopted for medical image analysis in recent years given its promising performance in image segmentation and classification tasks. The success of m…
QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results
Raghav Mehta, Angelos Filos, Ujjwal Baid +89
Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges.…