Medical Image
Medical image analysis focuses on extracting meaningful information from various imaging modalities (e.g., CT, MRI, X-ray) to improve diagnosis and treatment planning. Current research emphasizes developing robust and efficient algorithms, often employing convolutional neural networks (CNNs), transformers, and diffusion models, to address challenges like data variability, limited annotations, and privacy concerns. These advancements are crucial for improving the accuracy and speed of medical image analysis, leading to better patient care and accelerating medical research.
Papers
Rethinking Perceptual Metrics for Medical Image Translation
Nicholas Konz, Yuwen Chen, Hanxue Gu, Haoyu Dong, Maciej A. Mazurowski
MedRG: Medical Report Grounding with Multi-modal Large Language Model
Ke Zou, Yang Bai, Zhihao Chen, Yang Zhou, Yidi Chen, Kai Ren, Meng Wang, Xuedong Yuan, Xiaojing Shen, Huazhu Fu
MedIAnomaly: A comparative study of anomaly detection in medical images
Yu Cai, Weiwen Zhang, Hao Chen, Kwang-Ting Cheng
Cascaded Multi-path Shortcut Diffusion Model for Medical Image Translation
Yinchi Zhou, Tianqi Chen, Jun Hou, Huidong Xie, Nicha C. Dvornek, S. Kevin Zhou, David L. Wilson, James S. Duncan, Chi Liu, Bo Zhou
RetiGen: A Framework for Generalized Retinal Diagnosis Using Multi-View Fundus Images
Ze Chen, Gongyu Zhang, Jiayu Huo, Joan Nunez do Rio, Charalampos Komninos, Yang Liu, Rachel Sparks, Sebastien Ourselin, Christos Bergeles, Timothy Jackson
Anytime, Anywhere, Anyone: Investigating the Feasibility of Segment Anything Model for Crowd-Sourcing Medical Image Annotations
Pranav Kulkarni, Adway Kanhere, Dharmam Savani, Andrew Chan, Devina Chatterjee, Paul H. Yi, Vishwa S. Parekh
Medical Image Data Provenance for Medical Cyber-Physical System
Vijay Kumar, Kolin Paul