CT Image
Computed tomography (CT) imaging produces detailed 3D images of the body, crucial for diagnosis and treatment planning across various medical specialties. Current research emphasizes improving CT image analysis through deep learning, focusing on architectures like U-Nets, Vision Transformers, and diffusion models for tasks such as organ segmentation, lesion detection, and image enhancement. These advancements aim to increase diagnostic accuracy, improve treatment planning, reduce radiation exposure, and enable more efficient workflows in clinical settings.
Papers
DeepTechnome: Mitigating Unknown Bias in Deep Learning Based Assessment of CT Images
Simon Langer, Oliver Taubmann, Felix Denzinger, Andreas Maier, Alexander Mühlberg
Decoupled Pyramid Correlation Network for Liver Tumor Segmentation from CT images
Yao Zhang, Jiawei Yang, Yang Liu, Jiang Tian, Siyun Wang, Cheng Zhong, Zhongchao Shi, Yang Zhang, Zhiqiang He
Survival Analysis for Idiopathic Pulmonary Fibrosis using CT Images and Incomplete Clinical Data
Ahmed H. Shahin, Joseph Jacob, Daniel C. Alexander, David Barber
Classifications of Skull Fractures using CT Scan Images via CNN with Lazy Learning Approach
Md Moniruzzaman Emon, Tareque Rahman Ornob, Moqsadur Rahman