Medical Image Data
Medical image data analysis focuses on extracting meaningful information from various imaging modalities (e.g., CT, MRI, X-ray) to improve diagnosis and treatment. Current research emphasizes robust feature extraction techniques, often employing deep learning models like diffusion probabilistic models, hypernetworks, and convolutional neural networks, to address challenges such as data variability and limited annotations. These advancements aim to enhance the accuracy and reliability of automated diagnostic tools, improve the efficiency of clinical workflows, and ultimately contribute to better patient care.
Papers
One Copy Is All You Need: Resource-Efficient Streaming of Medical Imaging Data at Scale
Pranav Kulkarni, Adway Kanhere, Eliot Siegel, Paul H. Yi, Vishwa S. Parekh
DeepMediX: A Deep Learning-Driven Resource-Efficient Medical Diagnosis Across the Spectrum
Kishore Babu Nampalle, Pradeep Singh, Uppala Vivek Narayan, Balasubramanian Raman