Medical Imaging
Medical imaging research focuses on developing and improving AI-powered methods for analyzing medical images, primarily aiming to enhance diagnostic accuracy, efficiency, and accessibility. Current research emphasizes robust model architectures (like Vision Transformers and UNets) and algorithms (including federated learning, generative adversarial networks, and diffusion models) to address challenges such as data scarcity, domain shifts (e.g., scanner variations), and privacy concerns. These advancements hold significant potential for improving clinical decision-making, particularly in areas with limited radiologist access, and for facilitating more efficient and reliable medical diagnoses.
Papers
Counterfactual MRI Data Augmentation using Conditional Denoising Diffusion Generative Models
Pedro Morão, Joao Santinha, Yasna Forghani, Nuno Loução, Pedro Gouveia, Mario A. T. Figueiredo
Enhancing Brain Tumor Classification Using TrAdaBoost and Multi-Classifier Deep Learning Approaches
Mahin Mohammadi, Saman Jamshidi
Scalable Drift Monitoring in Medical Imaging AI
Jameson Merkow, Felix J. Dorfner, Xiyu Yang, Alexander Ersoy, Giridhar Dasegowda, Mannudeep Kalra, Matthew P. Lungren, Christopher P. Bridge, Ivan Tarapov
Adversarial Neural Networks in Medical Imaging Advancements and Challenges in Semantic Segmentation
Houze Liu, Bo Zhang, Yanlin Xiang, Yuxiang Hu, Aoran Shen, Yang Lin