- The model uses a single U-Net SeResNext101 architecture with Convolutional Block Attention Module (CBAM), hypercolumns, and deep supervision.
- It reads the WSIs as tiled 1024x1024 pixel images and then further resized as 320x320 tiles and sampled using a balanced sampling strategy.
- The model is trained using a combination of Binary Cross-entropy loss and Lovász Hinge loss, and the optimizer used is SGD (Stochastic gradient descent). Training is for 20 epochs, with a learning rate of 1e-4 to 1e-6 and batch size of 8.
- For the model trained on colon data from scratch or using transfer learning, the training is done for 50-100 epochs and the validation set is increased from 1 slide to 2 slides.
- albumentations
- opencv-python
- pandas
- torch
- tqdm
- Python 3
- CUDA
- cuddn
- nvidia drivers
- See
kaggle-hubmap-main/requirements.txtfile for a detailed list of dependencies.
- Use Inference.py to run inference on a dataset.
- Use train folder to train on a dataset. cd src cd 01_data_preparation/01_01 python data_preparation_01_01.py cd .. cd 01_02 python data_preparation_01_01.py cd .. cd .. cd 02_train python train_02.py