turningUNet
turningUNet is a variant of the U-Net convolutional neural network architecture designed for medical image segmentation tasks. The original U-Net, introduced by Olaf Ronneberger et al. in 2015, proved highly effective for biomedical image segmentation due to its encoder-decoder structure and skip connections, which allow for precise localization and context aggregation.
turningUNet builds upon this foundation by incorporating modifications specifically aimed at improving performance on certain types
The primary goal of turningUNet, like other U-Net derivatives, is to accurately delineate regions of interest