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Medical images differ from natural images in significantly higher resolutionsand smaller regions of interest. Because of these differences, neural networkarchitectures that work well for natural images might not be applicable tomedical image analysis. In this work, we extend the globally-aware multipleinstance classifier, a framework we proposed to address these unique propertiesof medical images. This model first uses a low-capacity, yet memory-efficient,network on the whole image to identify the most informative regions. It thenapplies another higher-capacity network to collect details from chosen regions.Finally, it employs a fusion module that aggregates global and localinformation to make a final prediction. While existing methods often requirelesion segmentation during training, our model is trained with only image-levellabels and can generate pixel-level saliency maps indicating possible malignantfindings. We apply the model to screening mammography interpretation:predicting the presence or absence of benign and malignant lesions. On the NYUBreast Cancer Screening Dataset, consisting of more than one million images,our model achieves an AUC of 0.93 in classifying breasts with malignantfindings, outperforming ResNet-34 and Faster R-CNN. Compared to ResNet-34, ourmodel is 4.1x faster for inference while using 78.4% less GPU memory.Furthermore, we demonstrate, in a reader study, that our model surpassesradiologist-level AUC by a margin of 0.11. The proposed model is availableonline: https://github.com/nyukat/GMIC.

An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localization