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Found 3 papers, 2 papers with code
Deep Multi-instance Networks with Sparse Label Assignment for Whole Mammogram Classification
Inspired by the success of using deep convolutional features for natural image analysis and multi-instance learning (MIL) for labeling a set of instances/patches, we propose end-to-end trained deep multi-instance networks for mass classification based on whole mammogram without the aforementioned ROIs.
HLA class I binding prediction via convolutional neural networks
We then propose a deep convolutional neural network architecture, name HLA-CNN, for the task of HLA class I-peptide binding prediction.
Deep Multi-instance Networks with Sparse Label Assignment for Whole Mammogram Classification
Inspired by the success of using deep convolutional features for natural image analysis and multi-instance learning for labeling a set of instances/patches, we propose end-to-end trained deep multi-instance networks for mass classification based on whole mammogram without the aforementioned costly need to annotate the training data.
