text:   images utilizing weakly supervised localization",7,"['We just released a new paper on deep learning for screening mammography!\n\n“An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localization”.\n\n<LINK>\n\n1/7 <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.\n\n2/7 https://t.co/d0PdA0ZBg9', '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).\n\n3/7 https://t.co/YsoNpVmhjs', '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, ...\n\n4/7 https://t.co/4uZr23aEcC', '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.\n\n5/7 https://t.co/I4S9N87lcX', 'We made the code and the model public at https://t.co/xATXueLLWw. We are hoping to enable others to experiment with and build upon our model! Please let us know if you find it useful.\n\n6/7', '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.\n\n7/7']",20,02,1674