mld3 / deep-learning-applied-to-chest-x-rays-exploiting-and-preventing-shortcuts Goto Github PK

This is the code repository for the manuscript "Deep Learning Applied to Chest X-Rays: Exploiting and Preventing Shortcuts" and video.

• preprocess_mimic_attributes/: contains a script to link MIMIC-CXR and MIMIC-IV to extract attribute features. Note that the link between MIMIC-CXR and MIMIC-IV was not publicly available at the time of publication, so this was added after the fact to enable other researchers to recreate more of our experiments.

• preprocessing/ contains code for subsampling the datasets or assigned bias label for synthetic image preprocessing experiments

• dataset/ contains data loaders

• model/ contains model loader

• standardizers/ is where the mean and standard deviation should be saved if you're standardizing to a subset of the data (e.g., when there is too much data to load onto disk)

Follow directions to download the MIMIC-CXR, MIMIC-IV, and CheXpert datasets

Run resampling_skewed_unskewed.ipynp in preprocessing/ to subsample the data into skewed and unskewed datasets

Run assumption_relaxation_bias_assignment.ipynp and random_bias_assignment.ipynb in preprocessing/synthetic_shortcuts/ to assign bias labels, and then apply_filter.ipynb to apply the Gaussian filter to the images

Pre-specified arguments can be set in config.json:

Required arguments:

• csv_file: Path to metadata file.
• checkpoint: Path to file location where model checkpoints will be saved.
• labels: column name in the metadata files of the target classes. These can be separated by "|" (e.g., CHF|Pneumonia)
• rotate degrees: degrees of rotation to use for random rotation in image augmentation.
• disk: if disk = 1, all images will be loaded into memory before training. Otherwise during training images will be fetched from disk.
• mask : mask = 1 if masked loss will be used (i.e., if there are missing labels). All missing labels in the metadata file should be set to -1.
• early_stop: early_stop = 1 if early stopping criteria will be used. Otherwise model will train to 3 epochs.
• pretrain: Whether or not to use an initialization. If pretrain is "yes", then ImageNet initialization will be used unless a pretrain file is specified. Otherwise, pretrain should be "random"
• pretrain_file: file path to pretrained model (i.e., source task model or pretrained model on MIMIC-CXr and CheXpert)
• pretrain_classes: number of target labels pretrain model had
• loader_names : list of split names (i.e., ["train", "valid", "test"]). You do not have to include "test".

Optional arguments: These specify the number of layers to train. If none of are specified, the whole network will be trained.

• tune_classifier: 1 to t train the final fully connected layer of the network
• sensitivity_analysis: 1 to train a variable number of blocks in the network. If 1, the num_blocks argument should be specified.
• num_blocks: number of denseblocks to train (increasing from the end of the network to the beginning) between 1 and 3.

The following exmple code will train a model using train.py. Each run requires that a model_name and model_type be specificied. There are pre-specified in the config file along with other parameters (described in further detail below). Models will be saved in the directory chexpoint/model_type/model_name.

python train.py --model_type example_model_type --model_name example_model_name

Other non-required arguments are:

• gpu: specify the gpu numbers to train on, default is 0 and 1.
• budget: number of hyperparameter combinations to try. Default is 50.
• repeats: number of seed initializations to try. Default is 3.
• save_every: for pretraining on MIMIC-CXR and CheXpert. Number of iterations to complete before saving a checkpoint. Default is None and will save after every epoch.
• save_best_num, for pretraining on MIMIC-CXR and CheXpert. Number of top checkpoints to save (based on best AUROC performance on the validation set). Default is 1.
• optimizer: optimzier to use. Default is "sgd", but can also choose "adam" for pretraining on MIMIC-CXR and CheXpert.

To train a model on MIMIC-CXR and CheXpert, you'll want to use the save_every, save_best_num, and optimizer arguments. This will train on an ImageNet initialized model:

python train.py --model_type example_model_type --model_name example_model_name --save_every 4800 --save_best_num 10 --optimizer adam

To train a model after pretraining on either MIMIC-CXR/CheXpert or a source task, you'll need to specify the file location of the pretrained model in the config file.