IEEE Transaction on Medical Imaging, 2024

[Journal Link] | [arXiv] | [PseMix Walkthrough] | [WSI Preprocessing] | [Related Resources] | [Citation]

Abstract: Multiple instance learning (MIL) has become one of the most important frameworks for gigapixel Whole Slide Images (WSIs). In current practice, most MIL networks often face two unavoidable problems in training: i) insufficient WSI data and ii) the sample memorization inclination inherent in neural networks. To address these problems, this paper proposes a new Pseudo-bag Mixup (PseMix) data augmentation scheme, inspired by the basic idea of Mixup. Cooperated by pseudo-bags, this scheme fulfills the critical size alignment and semantic alignment in Mixup. Moreover, it is efficient and plugin-and-play, neither involving time-consuming operations nor relying on model predictions. Experimental results show that PseMix could often improve the performance of state-of-the-art MIL networks. Most importantly, it could also boost the generalization performance of MIL models in special test scenarios, and promote their robustness to patch occlusion and label noise.

📚 Recent updates:

• 24/05/27: 🔥 new SOTA performance with PseMix using CONCH features
• 24/05/19: add data_split and Notebook - Generate Data Split
• 24/05/09: add two PseMix Walkthrough notebooks: Notebook - Pseudo-bag Generation and Notebook - Pseudo-bag Mixup
• 24/05/08: add link to a detailed tutorial of WSI preprocessing
• 24/02/27: add missing codes regrading the module of optim
• 24/02/22: add useful research papers involving pseudo-bags

Adopting PseMix in training MIL networks could

(1) improve performance with minimal costs:

(2) obtain better generalization and robustness:

Training curves, showing the AUC performance on training and test data (exported from wandb), are given below.

(3) often obtain improvements when using the SOTA feature extractor CONCH:

🔥 New SOTA results on TCGA-BRCA, tested with the patch features extracted by CONCH:

There are some fun observations from the table above:

• Strong feature extractor could boost the performance by a large margin
• ABMIL is a very strong baseline, outperforming DSMIL and TransMIL when using CONCH features
• Training with PseMix often leads to SOTA performance

PseMix contains two key steps: i) pseudo-bag generation and ii) pseudo-bag mixup. Here are two alternative means that could help you quickly understand the two key steps.

Get started with PseMix:

• Notebook - Pseudo-bag Generation;
• Notebook - Pseudo-bag Mixup.

An overall description of the two key steps is as follows:

Pseudo-bag generation contains two sub-steps:

• Phenotype clustering by
  • bag-prototype-based instance clustering
  • cluster fine-tuning
• Phenotype-stratified sampling

Its implementation details can be found via the function generate_pseudo_bags. This predefined function will be directly used and called in the next step, pseudo-bag mixup.

Below is the pseudo-code of pseudo-bag mixup:

# generate_pseudo_bags: function for dividing WSI bags into pseudo-bags
# ALPHA: the hyper-parameter of Beta distribution
# N: the number of pseudo-bags in each WSI bag
# PROB_MIXUP: random mixing parameter for determining the proportion of mixed bags. 
for (X, y) in loader: # load a minibatch 
    n_batch = X.shape[0] # with `n_batch` WSI bags (samples)

    # 1. dividing each bag into `N` pseudo-bags
    X = generate_pseudo_bags(X)

    new_idxs = torch.randperm(n_batch)
    # draw a mixing scale from Beta distribution
    lam = numpy.random.beta(ALPHA, ALPHA) 
    lam = min(lam, 1.0 - 1e-5) # avoid numerical overflow when transforming it into discrete ones
    lam_discrete = int(lam * (N + 1)) # transform into discrete values

    # 2. pseudo-bag-level Mixup generates samples (new_X, new_y)
    new_X, new_y = [], []
    for i in range(n_batch):
    	# randomly select pseudo-bags according to `lam_discrete`
        masked_bag_A = select_pseudo_bags(X[i], lam_discrete) # select `lam_discrete` pseudo-bags
        masked_bag_B = select_pseudo_bags(X[new_idxs[i]], N - lam_discrete) # select `n-lam_discrete` pseudo-bags

        # random-mixing mechanism for two purposes: more data diversity and efficient learning on mixed samples.
        if np.random.rand() <= PROB_MIXUP:
            mixed_bag = torch.cat([masked_bag_A, masked_bag_B], dim=0) # instance-axis concat
            new_X.append(mixed_bag)
            mix_ratio = lam_discrete / N
        else:
            masked_bag = masked_bag_A 
            new_X.append(masked_bag)
            mix_ratio = 1.0

        # target-level mixing
        new_y.append(mix_ratio * y[i] + (1 - mix_ratio) * y[new_idxs[i]]) 

    # 3. minibatch training
    minibatch_training(new_X, new_y)

More details can be found at

• pseudo-bag-level Mixup.
• training with mixed labels.
• weighted loss for mixed samples, following the implementation of Mixup.

The procedure of WSI preprocessing is elaborated in Pipeline-Processing-TCGA-Slides-for-MIL. Please move to it for a detailed tutorial.

Using the following command to load running configurations from a yaml file and train the model:

python3 main.py --config config/cfg_clf_mix.yml --handler clf --multi_run

The configurations that we need to pay attention are as follows:

• Dataset (we process WSIs with CLAM)
  • path_patch: the directory path to patch files.
  • path_table: the file path of a csv table that contains WSI IDs and their label information.
  • data_split_path: the file path of a npz file that stores data splitting information.
• Network
  • net_dims: the setting of embedding dimension, e.g., 1024-256-2.
  • backbone: network backbone, one of ABMIL, DSMIL, and TransMIL.
• Pseudo-bag Dividing
  • pseb_dividing: the method used to divide instances, one of proto, kmeans, and random.
  • pseb_n: the number of pseudo-bags for each WSI bag, 30 by default.
  • pseb_l: the number of phenotypes, 8 by default.
  • pseb_iter_tuning: the number of fine-tuning iterations, 8 by default.
  • pseb_mixup_prob: the probability of random-mixing.
• Pseudo-bag Mixup
  • mixup_type: the method of Mixup, psebmix by default.
  • mixup_alpha: the parameter of beta distribution, i.e., the value of alpha.

Other configurations are explained in config/cfg_clf_mix.yml. They could remain as before without any changes.

Here we list the related works involving pseudo-bags or using pseudo-bags for training deep MIL networks.

NOTE: please open a new PR if you want to add your work in this resource list.

If you find this work helps your research, please consider citing our paper:

@article{liu10385148,
  author={Liu, Pei and Ji, Luping and Zhang, Xinyu and Ye, Feng},
  journal={IEEE Transactions on Medical Imaging}, 
  title={Pseudo-Bag Mixup Augmentation for Multiple Instance Learning-Based Whole Slide Image Classification}, 
  year={2024},
  volume={43},
  number={5},
  pages={1841-1852},
  doi={10.1109/TMI.2024.3351213}
}

or P. Liu, L. Ji, X. Zhang and F. Ye, "Pseudo-Bag Mixup Augmentation for Multiple Instance Learning-Based Whole Slide Image Classification," in IEEE Transactions on Medical Imaging, vol. 43, no. 5, pp. 1841-1852, May 2024, doi: 10.1109/TMI.2024.3351213.