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Found 5 papers, 2 papers with code
Evaluating the Performance of StyleGAN2-ADA on Medical Images
Our computational ablation study revealed that transfer learning and data augmentation stabilize training and improve the perceptual quality of the generated images.
Ranked #1 on
Medical Image Generation
on ACDC
Integrative Imaging Informatics for Cancer Research: Workflow Automation for Neuro-oncology (I3CR-WANO)
Mean Dice scores were 0. 882 ($\pm$0. 244) and 0. 977 ($\pm$0. 04) for whole tumor segmentation for WUSM and MDA, respectively.
Automated WBRT Treatment Planning via Deep Learning Auto-Contouring and Customizable Landmark-Based Field Aperture Design
In this work, we developed and evaluated a novel pipeline consisting of two landmark-based field aperture generation approaches for WBRT treatment planning; they are fully automated and customizable.
Correlation between image quality metrics of magnetic resonance images and the neural network segmentation accuracy
Deep neural networks with multilevel connections process input data in complex ways to learn the information. A networks learning efficiency depends not only on the complex neural network architecture but also on the input training images. Medical image segmentation with deep neural networks for skull stripping or tumor segmentation from magnetic resonance images enables learning both global and local features of the images. Though medical images are collected in a controlled environment, there may be artifacts or equipment based variance that cause inherent bias in the input set. In this study, we investigated the correlation between the image quality metrics of MR images with the neural network segmentation accuracy. For that we have used the 3D DenseNet architecture and let the network trained on the same input but applying different methodologies to select the training data set based on the IQM values. The difference in the segmentation accuracy between models based on the random training inputs with IQM based training inputs shed light on the role of image quality metrics on segmentation accuracy. By running the image quality metrics to choose the training inputs, further we may tune the learning efficiency of the network and the segmentation accuracy.
PocketNet: A Smaller Neural Network for Medical Image Analysis
Medical imaging deep learning models are often large and complex, requiring specialized hardware to train and evaluate these models.
