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
Context-aware Self-supervised Learning for Medical Images Using Graph Neural Network
Li Sun, Ke Yu, Kayhan Batmanghelich
Towards the Use of Saliency Maps for Explaining Low-Quality Electrocardiograms to End Users
Ana Lucic, Sheeraz Ahmad, Amanda Furtado Brinhosa, Vera Liao, Himani Agrawal, Umang Bhatt, Krishnaram Kenthapadi, Alice Xiang, Maarten de Rijke, Nicholas Drabowski
Learning Underrepresented Classes from Decentralized Partially Labeled Medical Images
Nanqing Dong, Michael Kampffmeyer, Irina Voiculescu
Augment like there's no tomorrow: Consistently performing neural networks for medical imaging
Joona Pohjonen, Carolin Stürenberg, Atte Föhr, Reija Randen-Brady, Lassi Luomala, Jouni Lohi, Esa Pitkänen, Antti Rannikko, Tuomas Mirtti
Data Stealing Attack on Medical Images: Is it Safe to Export Networks from Data Lakes?
Huiyu Li, Nicholas Ayache, Hervé Delingette
Transformer-based Personalized Attention Mechanism for Medical Images with Clinical Records
Yusuke Takagi, Noriaki Hashimoto, Hiroki Masuda, Hiroaki Miyoshi, Koichi Ohshima, Hidekata Hontani, Ichiro Takeuchi