Overcoming Computational Bottlenecks In Large-Scale Medical Image Segmentation Using Optimized U-Net
Training nnU-Net models for medical image segmentation with large patient samples is computationally expensive, limiting iteration speed in research and clinical translation. We present an optimized training workflow that significantly accelerates nnU-Net tra...
Poster Program · Therapy Physics