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
A Generalist Learner for Multifaceted Medical Image Interpretation
Hong-Yu Zhou, Subathra Adithan, Julián Nicolás Acosta, Eric J. Topol, Pranav Rajpurkar
MoVL:Exploring Fusion Strategies for the Domain-Adaptive Application of Pretrained Models in Medical Imaging Tasks
Haijiang Tian, Jingkun Yue, Xiaohong Liu, Guoxing Yang, Zeyu Jiang, Guangyu Wang
ConPro: Learning Severity Representation for Medical Images using Contrastive Learning and Preference Optimization
Hong Nguyen, Hoang Nguyen, Melinda Chang, Hieu Pham, Shrikanth Narayanan, Michael Pazzani
Research on Intelligent Aided Diagnosis System of Medical Image Based on Computer Deep Learning
Jiajie Yuan, Linxiao Wu, Yulu Gong, Zhou Yu, Ziang Liu, Shuyao He
WangLab at MEDIQA-M3G 2024: Multimodal Medical Answer Generation using Large Language Models
Ronald Xie, Steven Palayew, Augustin Toma, Gary Bader, Bo Wang
On-the-Fly Point Annotation for Fast Medical Video Labeling
Meyer Adrien, Mazellier Jean-Paul, Jeremy Dana, Nicolas Padoy
DynaMMo: Dynamic Model Merging for Efficient Class Incremental Learning for Medical Images
Mohammad Areeb Qazi, Ibrahim Almakky, Anees Ur Rehman Hashmi, Santosh Sanjeev, Mohammad Yaqub