HACL-Net

This is the pytorch implementation of MICCAI 2023, "HACL-Net: Hierarchical Attention and Contrastive Learning Network for MRI-Based Placenta Accreta Spectrum Diagnosis".

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Citation

Lu, M., Wang, T., Zhu, H., Li, M. (2023). HACL-Net: Hierarchical Attention and Contrastive Learning Network for MRI-Based Placenta Accreta Spectrum Diagnosis. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14226. Springer, Cham. https://doi.org/10.1007/978-3-031-43990-2_29

Dependencies

Our experiments are implemented on the following packages. It is recommended to use anaconda to manage your python packages.

• Ubuntu 16.04
• Python 3.7.11
• PyTorch 1.10 / torchvision 0.11.2
• NVIDIA CUDA 11.3
• Numpy 1.19.5
• scikit-learn 1.0.2
• tqdm 4.62.3
• pandas 1.3.5

Train

1. Save all patient information, including pid, img_path, label in a csv file.

2. Save each patient's all MRI slices and the corresponding patient-level label into one npz file. For example, a patient with pid=1,should correspond to a file named 1.npz.

3. Specify your hyperparamter in train/main.py and train the model.

   bash bin/train.sh