README.md

DDA: Dimensionality Driven Augmentation Search for Contrastive Learning in Laparoscopic Surgery

Code for MIDL2024 paper "DDA: Dimensionality Driven Augmentation Search for Contrastive Learning in Laparoscopic Surgery"

---

DDAug pipeline

# Method of Moments estimation of LID
def lid_mom_est(data, reference, k, get_idx=False):
    b = data.shape[0]
    k = min(k, b-2)
    data = torch.flatten(data, start_dim=1)
    reference = torch.flatten(reference, start_dim=1)
    r = torch.cdist(data, reference, p=2)
    a, idx = torch.sort(r, dim=1)
    m = torch.mean(a[:, 1:k], dim=1)
    lids = m / (a[:, k] - m)
    if get_idx:
        return idx, lids
    return lids

# features: representations that need LID to be estimated. 
# reference: reference representations, usually, the same batch of representations can be used. 
# k: locality parameter, the neighbourhood size. 
# NOTE: features and reference should be in the same dimension.

lids = lid_mom_est(data=features, reference=full_rank_features.detach(), k=k)
loss = - torch.abs(torch.log(lids/1)).mean() # Eq (5) of the paper. 
        

---

Reproduce results from the paper

We provide configuration files in the configs folder. Details of all necessary hyperparameters are also in the Appendix of the paper.

Pretrained models are available here in this Google Drive folder.

An example of how to pretrain the base model:

srun python3 -u main_simclr_kornia.py --exp_name      $exp_name     \
                                      --exp_path      $exp_path     \
                                      --exp_config    $exp_config   \
                                      --ddp --dist_eval --seed $seed

An example of how to run an augmentation search:

srun python3 -u main_aug_search.py --exp_name      $exp_name     \
                                   --exp_path      $exp_path     \
                                   --exp_config    $exp_config   \
                                   --ddp --dist_eval --seed $seed        

An example of how to run linear probing:

srun python3 -u main_linear_prob.py --exp_name      $exp_name     \
                                    --exp_path      $exp_path     \
                                    --exp_config    $exp_config   \
                                    --seed          $seed         \
                                    --ddp        

An example of how to run finetuning:

srun python3 -u train_ddp.py --exp_name      $exp_name     \
                             --exp_path      $exp_path     \
                             --exp_config    $exp_config   \
                             --seed          $seed         \
                             --ddp     

Citation

If you use this code in your work, please cite the accompanying paper:

@inproceedings{
zhou2024dda,
title={{DDA}: Dimensionality Driven Augmentation Search for Contrastive Learning in Laparoscopic Surgery},
author={Yuning Zhou and Henry Badgery and Matthew Read and James Bailey and Catherine Davey},
booktitle={Medical Imaging with Deep Learning},
year={2024}
}

Part of the code is based on the following repo:

• PyTorch implementation of LDReg: https://github.com/HanxunH/LDReg
• PyTorch implementation of Faster AutoAugment: https://github.com/moskomule/dda