add RetinaUNet model into Pyhealth, expand usage with sample notebook.#979
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iamkishann wants to merge 23 commits intosunlabuiuc:masterfrom
Draft
add RetinaUNet model into Pyhealth, expand usage with sample notebook.#979iamkishann wants to merge 23 commits intosunlabuiuc:masterfrom
iamkishann wants to merge 23 commits intosunlabuiuc:masterfrom
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This implementation provides a clean Retina U-Net model for medical image object detection, utilizing standard PyTorch components. It includes various blocks for convolution, residual connections, anchor generation, and heads for classification, bounding box regression, and segmentation. Still on development
Add refactoring to the missing parts of the Initial Retina Unet model implementation based on the original code from the paper repository
Implement training pipeline for Retina U-Net model with logging, device handling, and loss computation.
This script generates a dummy dataset for testing the Retina U-Net training pipeline, creating synthetic images with segmentation masks. It includes functions to create images and masks, as well as to generate and save a dataset with metadata.
This file implements a data loader for the LIDC-IDRI dataset, compatible with the PyHealth framework. It includes functionality for loading images and masks, normalizing, resizing, and applying data augmentation.
This script tests the training pipeline of the Retina U-Net model using a dummy dataset. It verifies data generation, loading, model forward pass, and training loop functionality.
Refactor loss computation and update model parameters.
… docs, remove deprecated alias and print statement
…and label_keys parameters
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add model -> RetiaUnet
add tests -> core/test_retina_unet.py
add example notebook usage and training -> examples/training_retina_unet.ipynb