text: 254,55,1230644029311901703,494134136,Krzysztof Geras,"We just released a new paper on deep learning for screening mammography! “An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localization”. <LINK> 1/7 <LINK> Despite learning only with image-level labels, the model achieves an AUC of 0.93, outperforming ResNet-34 and Faster R-CNN. Compared to ResNet-34, it is 4.1x faster while using 78.4% less memory. In a reader study, it surpasses radiologist-level AUC by a margin of 0.11. 2/7 <LINK> Our model works in 3 stages. (1) Looking at the entire image with a network of a relatively low capacity to identify the most informative patches. (2) Looking at these patches with a network of a higher capacity. (3) Integrating information obtained in stages (1) and (2). 3/7 <LINK> We conducted extensive experiments on the importance of various design choices in the model, the type of pooling aggregating saliency maps, the number of patches extracted to examine further, ... 4/7 <LINK> We are expecting that our model will be especially useful when applied to data for which it is difficult or impossible to collect pixel-level annotations. We also hypothesize that this or similar models could be useful to discover new biomarkers. 5/7 <LINK> We made the code and the model public at <LINK>. We are hoping to enable others to experiment with and build upon our model! Please let us know if you find it useful. 6/7 This paper is another product of a collaboration between @cai2r and @NYUDataScience. It was led by @ArtieShen, supported by @NanWu__, @zhansheng, @jpatrickpark, Kangning Liu, @TyagiSudarshini, Laura Heacock, S. Gene Kim, @DrLindaMoy, @kchonyc and myself. 7/7",http://arxiv.org/abs/2002.07613,"Medical images differ from natural images in significantly higher resolutions and smaller regions of interest. Because of these differences, neural network architectures that work well for natural images might not be applicable to medical image analysis. In this work, we extend the globally-aware multiple instance classifier, a framework we proposed to address these unique properties of medical images. This model first uses a low-capacity, yet memory-efficient, network on the whole image to identify the most informative regions. It then applies another higher-capacity network to collect details from chosen regions. Finally, it employs a fusion module that aggregates global and local information to make a final prediction. While existing methods often require lesion segmentation during training, our model is trained with only image-level labels and can generate pixel-level saliency maps indicating possible malignant findings. We apply the model to screening mammography interpretation: predicting the presence or absence of benign and malignant lesions. On the NYU Breast Cancer Screening Dataset, consisting of more than one million images, our model achieves an AUC of 0.93 in classifying breasts with malignant findings, outperforming ResNet-34 and Faster R-CNN. Compared to ResNet-34, our model is 4.1x faster for inference while using 78.4% less GPU memory. Furthermore, we demonstrate, in a reader study, that our model surpasses radiologist-level AUC by a margin of 0.11. The proposed model is available online: this https URL ","An interpretable classifier for high-resolution breast cancer screening