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rithmic screening consists of designing both a screening classifier, which estimates the probability that a candidate is qualified, and a screening policy, which shortlists candidates using the candidates' probability values estimated by the screening classifier. Only very recently, a line of work has focused specifically on algorithmic
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WhatsApp: +86 18221755073An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localization December 2020 Medical Image Analysis 68(Feb):101908
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WhatsApp: +86 182217550731. Introduction. As the second leading cause of cancer death among women in the US, breast cancer has been studied for decades. While studies have shown screening mammography has significantly reduced breast cancer mortality, it is an imperfect tool [].To address its limitations, convolutional neural networks (CNN) designed for computer vision tasks on natural images …
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WhatsApp: +86 18221755073This is an implementation of the model used for breast cancer classification as described in our paper Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening.The implementation …
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WhatsApp: +86 18221755073A recent budget impact model considering LDCT as it is widely adopted in the United States indicated that LDCT screening would avoid up to 8,100 premature lung cancer deaths at a 75% screening rate with an additional screening cost of $240,000 to avoid one lung cancer death. 7 Complementing LDCT screening with a noninvasive biomarker test by ...
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WhatsApp: +86 18221755073Screening classifiers are increasingly used to identify qualified candidates in a variety of selection processes. In this context, it has been recently shown that if a classifier is calibrated, one can identify the smallest set of candidates which contains, in expectation, a desired number of qualified candidates using a threshold decision rule
WhatsApp: +86 18221755073@article{geras2017high, title = {High-resolution breast cancer screening with multi-view deep convolutional neural networks}, author = {Krzysztof J. Geras and Stacey Wolfson and Yiqiu Shen and Nan Wu and S. Gene Kim and Eric Kim and Laura Heacock and Ujas Parikh and Linda Moy and Kyunghyun Cho}, journal = {arXiv:1703.07047}, year = {2017} }
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WhatsApp: +86 18221755073A classifier combining chest X-ray (CXR) with clinical features may serve as a rapid screening approach. Methods: The study included 512 patients with COVID-19 and 106 with influenza A/B pneumonia. A deep neural network (DNN) was applied, and deep features derived from CXR and clinical findings formed fused features for diagnosis prediction.
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WhatsApp: +86 18221755073fdepend on the classifier and data distribu-tion. These results lend support to focusing on calibration as the only requirement for screening classifiers. In this work, we argue that screening policies given by threshold decision rules using calibrated classifiers may suffer from an under-studied type of unfairness—they may be biased against ...
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WhatsApp: +86 18221755073After applying the classifiers to the otherwise eligible population of VA Boston patients (see Real-world performance below), for each disease we randomly selected 10 patient records for which the classifier indicated the presence of a target disease (positive-per-classifier) and 10 records for which the classifier indicated the absence of the ...
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