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Zhu, Wentao, Qi Lou, Yeeleng Scott Vang, and Xiaohui Xie. "Deep Multi-instance Networks with Sparse Label Assignment for Whole Mammogram Classification." MICCAI 2017.
Base pretrained models and datasets in pytorch (MNIST, SVHN, CIFAR10, CIFAR100, STL10, AlexNet, VGG16, VGG19, ResNet, Inception, SqueezeNet)
DSMIL: Dual-stream multiple instance learning networks for tumor detection in Whole Slide Image
this project is about a deep convolutional neural network for breast cancer screening exam classification, trained and evaluated on on the public INbreast, MIAS and DSM databases.
the train&test.csv file used by DE-Ada* model
If you want to train a breast cancer classifier or a segmentation model using the CBIS-DDSM dataset, this repository may help you to extract the mammograms and the masks from the original folder ea…
Using Cooperative Multi-Agent Reinforcement Learning for Mammogram ROI Classification
Detection of tumors on mammography images
Inbreast cancer detection and classification
Public repo for the ICCV2021-CVAMD paper "Is it Time to Replace CNNs with Transformers for Medical Images?"
Mammographic Image Classification with Deep Fusion Learning
Computer aided detection using Faster-RCNN
Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening
Deep Learning to Improve Breast Cancer Detection on Screening Mammography
Development of deep learning tool to classify breast cancer images from CBIS-DDSM, INBreast and MIAS databases.
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