How I Trained Mask R-CNN for Lung Segmentation

Learn to train a custom Mask R-CNN model for lung segmentation using PyTorch in this comprehensive 26-minute tutorial. Master the complete pipeline from preprocessing medical image datasets with masks to building a trained deep learning model that accurately predicts lung regions from chest scans. Discover how to implement transfer learning with ResNet-50 backbone, utilize PyTorch's built-in torchvision models, and preprocess medical imaging data effectively. Explore techniques for running inference on new images, visualizing segmentation masks, and saving models for production deployment. Follow along with hands-on demonstrations covering installation requirements, data preparation workflows, model architecture setup, training procedures, and testing methodologies. Gain practical experience with medical imaging applications while building foundational skills in instance segmentation using one of the most powerful computer vision architectures available.