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
End-to-end autoencoding architecture for the simultaneous generation of medical images and corresponding segmentation masks
Aghiles Kebaili, Jérôme Lapuyade-Lahorgue, Pierre Vera, Su Ruan
MSE-Nets: Multi-annotated Semi-supervised Ensemble Networks for Improving Segmentation of Medical Image with Ambiguous Boundaries
Shuai Wang, Tengjin Weng, Jingyi Wang, Yang Shen, Zhidong Zhao, Yixiu Liu, Pengfei Jiao, Zhiming Cheng, Yaqi Wang
UniMOS: A Universal Framework For Multi-Organ Segmentation Over Label-Constrained Datasets
Can Li, Sheng Shao, Junyi Qu, Shuchao Pang, Mehmet A. Orgun
EviPrompt: A Training-Free Evidential Prompt Generation Method for Segment Anything Model in Medical Images
Yinsong Xu, Jiaqi Tang, Aidong Men, Qingchao Chen
Domain Generalization by Learning from Privileged Medical Imaging Information
Steven Korevaar, Ruwan Tennakoon, Ricky O'Brien, Dwarikanath Mahapatra, Alireza Bab-Hadiasha
Revolutionizing Healthcare Image Analysis in Pandemic-Based Fog-Cloud Computing Architectures
Al Zahraa Elsayed, Khalil Mohamed, Hany Harb
Sam-Guided Enhanced Fine-Grained Encoding with Mixed Semantic Learning for Medical Image Captioning
Zhenyu Zhang, Benlu Wang, Weijie Liang, Yizhi Li, Xuechen Guo, Guanhong Wang, Shiyan Li, Gaoang Wang