This is the official implementation of the paper "Robust Deep AUC Maximization: A New Surrogate Loss and Empirical Studies on Medical Image Classification" published on ICCV2021.
pip install libaucBenchmark dataset contains Cat&Dog, CIFAR10, CIFAR100, STL10. To construct their imbalanced version, we show an example below:
from libauc.datasets import CIFAR10, CIFAR100, CAT_vs_DOG, STL10
(train_data, train_label), (test_data, test_label) = CIFAR10()from libauc.datasets import imbalance_generator
SEED = 123
imratio = 0.1 # postive_samples/(total_samples)
(train_images, train_labels) = imbalance_generator(train_data, train_label, imratio=imratio, shuffle=True, random_seed=SEED)
(test_images, test_labels) = imbalance_generator(test_data, test_label, is_balanced=True, random_seed=SEED)trainloader = torch.utils.data.DataLoader(ImageDataset(train_images, train_labels), batch_size=BATCH_SIZE, shuffle=True, num_workers=1, pin_memory=True, drop_last=True)
testloader = torch.utils.data.DataLoader( ImageDataset(test_images, test_labels, mode='test'), batch_size=BATCH_SIZE, shuffle=False, num_workers=1, pin_memory=True)For the instructions of training the models, please refer to this Notebook.
CheXpert is a large dataset of chest X-rays and competition, which consists of 224,316 chest radiographs of 65,240 patients.The details about the dataset can be found at https://stanfordmlgroup.github.io/competitions/chexpert/. The dataloader used in the paper can be downloaded here.
root="YOUR_DATA_PATH"
class_id="CLASS_ID"
traindSet = CheXpert(csv_path=root+'train.csv', image_root_path=root, use_frontal=True, image_size=224, mode='train', class_index=class_id)
testSet = CheXpert(csv_path=root+'valid.csv', image_root_path=root, use_frontal=True, image_size=224, mode='valid', class_index=class_id)
trainloader = torch.utils.data.DataLoader(traindSet, batch_size=32, num_workers=2, shuffle=True)
testloader = torch.utils.data.DataLoader(testSet, batch_size=32, num_workers=2, shuffle=False)For the instructions of training the models, please refer to this Notebook.
If you find this repo helpful, please cite the following paper:
@inproceedings{yuan2021robust,
title={Large-scale Robust Deep AUC Maximization: A New Surrogate Loss and Empirical Studies on Medical Image Classification},
author={Yuan, Zhuoning and Yan, Yan and Sonka, Milan and Yang, Tianbao},
booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
year={2021}
}
If you have any questions, please contact us @ Zhuoning Yuan [[email protected]] and Tianbao Yang [[email protected]] or please open a new issue in the Github.
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