1 to 2 of 2 Results
Jul 14, 2022
Zöllner, Frank, 2022, "Synthesis of CT images from digital body phantoms using CycleGAN [dataset]", https://doi.org/10.11588/data/7NRFYC, heiDATA, V1
The potential of medical image analysis with neural networks is limited by the restricted availability of extensive data sets. The incorporation of synthetic training data is one approach to bypass this shortcoming, as synthetic data offer accurate annotations and unlimited data... |
Jun 22, 2022
Zöllner, Frank, 2022, "Multimodal ground truth datasets for abdominal medical image registration [data]", https://doi.org/10.11588/data/ICSFUS, heiDATA, V1
Sparsity of annotated data is a major limitation in medical image processing tasks such as registration. Registered multimodal image data are essential for the diagnosis of medical conditions and the success of interventional medical procedures. To overcome the shortage of data,... |