Showing results (191-200 of 1,331) with videos related to
Sort By:
Pageof 134
Journal of Imaging Informatics in Medicine|April 8, 2024
Cascade-EC Network: Recognition of Gastrointestinal Multiple Lesions Based on EfficientNet and CA_stm_RetinanetXudong Guo, Lei Xu, Shengnan Li, et al.Journal of Imaging Informatics in Medicine|March 26, 2024
Segmentation-Based Fusion of CT and MR ImagesPragya Gupta, Nishant JainJournal of Imaging Informatics in Medicine|April 2, 2024
End-to-End Multi-task Learning Architecture for Brain Tumor Analysis with Uncertainty Estimation in MRI ImagesMaria Nazir, Sadia Shakil, Khurram KhurshidJournal of Imaging Informatics in Medicine|April 8, 2024
An Automated Deep Learning-Based Framework for Uptake Segmentation and Classification on PSMA PET/CT Imaging of Patients with Prostate CancerYang Li, Maliha R Imami, Linmei Zhao, et al.Journal of Imaging Informatics in Medicine|February 5, 2024
Improving the Reliability of Peer Review Without a Gold StandardTarmo Äijö, Daniel Elgort, Murray Becker, et al.Journal of Imaging Informatics in Medicine|February 6, 2024
Predicting T-Cell Lymphoma in Children From 18F-FDG PET-CT Imaging With Multiple Machine Learning ModelsTaiyu Yang, Danyan Liu, Zexu Zhang, et al.Journal of Imaging Informatics in Medicine|February 6, 2024
A Multi-center Dental Panoramic Radiography Image Dataset for Impacted Teeth, Periodontitis, and Dental Caries: Benchmarking Segmentation and Classification TasksXiang Li, Xuan Ma, Yibai Zhao, et al.Journal of Imaging Informatics in Medicine|February 2, 2024
Personalized Impression Generation for PET Reports Using Large Language ModelsXin Tie, Muheon Shin, Ali Pirasteh, et al.Journal of Imaging Informatics in Medicine|September 3, 2024
Dual-Tree Complex Wavelet Pooling and Attention-Based Modified U-Net Architecture for Automated Breast Thermogram Segmentation and ClassificationLalit Garia, Hariharan MuthusamyJournal of Imaging Informatics in Medicine|September 4, 2024
Unsupervised and Self-supervised Learning in Low-Dose Computed Tomography Denoising: Insights from Training StrategiesFeixiang Zhao, Mingzhe Liu, Mingrong Xiang, et al.Pageof 134