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
Decomposing Disease Descriptions for Enhanced Pathology Detection: A Multi-Aspect Vision-Language Pre-training Framework
Vu Minh Hieu Phan, Yutong Xie, Yuankai Qi, Lingqiao Liu, Liyang Liu, Bowen Zhang, Zhibin Liao, Qi Wu, Minh-Son To, Johan W. Verjans
DALSA: Domain Adaptation for Supervised Learning From Sparsely Annotated MR Images
Michael Götz, Christian Weber, Franciszek Binczyk, Joanna Polanska, Rafal Tarnawski, Barbara Bobek-Billewicz, Ullrich Köthe, Jens Kleesiek, Bram Stieltjes, Klaus H. Maier-Hein
MedFLIP: Medical Vision-and-Language Self-supervised Fast Pre-Training with Masked Autoencoder
Lei Li, Tianfang Zhang, Xinglin Zhang, Jiaqi Liu, Bingqi Ma, Yan Luo, Tao Chen
A Domain Translation Framework with an Adversarial Denoising Diffusion Model to Generate Synthetic Datasets of Echocardiography Images
Cristiana Tiago, Sten Roar Snare, Jurica Sprem, Kristin McLeod
ProMISe: Promptable Medical Image Segmentation using SAM
Jinfeng Wang, Sifan Song, Xinkun Wang, Yiyi Wang, Yiyi Miao, Jionglong Su, S. Kevin Zhou