diminishes their performance. We examine the design and value of a bias-aware linear classification algorithm that accounts for … bias in input data, using breast cancer diagnosis as our specific setting. In this context, a referring physician makes a …

microscopy without the need of any feature extraction, using a minimum of images, improving work-flows that involve cancer cell … Neural Network in distinguishing between two cancer lines. In their approach, they trained, validated and tested their 6 …-layer CNN on 1,241 images of MDA-MB-468 and MCF7 breast cancer cell line in an end-to-end fashion, allowing the system to …

to bronchial and pulmonary cancer for insight into the root of the problem. An aging population has generated huge … expenditures in cancer care. This study proposed a two-stage cluster method by using k-means and a Self-Organizing Map (SOM) to … conduct a scientific analysis of the health insurance database of cancer prescription and patients in 2016. The findings of …

://ssrn.com/abstract=2908286) to 10,656 published exome samples for 32 cancer types. A majority of cancer types exhibit mutation clustering … across 14 cancer types. In contrast, we find in- and out-of-sample instabilities in cancer signatures extracted from exome …-stage cancer diagnostics such as novel blood-test methods currently in development …

-means to extracting cancer signatures from genome data without using nonnegative matrix factorization (NMF). *K …-means' computational cost is a fraction of NMF's. Using 1,389 published samples for 14 cancer types, we find that 3 cancers (liver cancer …, lung cancer and renal cell carcinoma) stand out and do not have cluster-like structures. Two clusters have especially high …