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
June 7, 2022
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
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