Algorithm Approach Seen to Accurately Detect High-Grade Breast Cancers in Early Study

Algorithm Approach Seen to Accurately Detect High-Grade Breast Cancers in Early Study
An approach used to detect damage in underwater marine structures may make identifying high-grade breast cancer cells in  histopathology images easier — and more accurate. The study, "Automated Segmentation of Nuclei in Breast Cancer Histopathology Images," published in PLOS One, was a result of joint work by doctors, engineers, and mathematicians from India and Ireland, and may move breast cancer screening one step closer to being an automated process. Currently, breast cancer diagnosis requires the use of the Blood-Richardson grading system. This relies on visual assessments by pathologists, who look at tumor sections under a microscope and grade the tumor according to their observations. But like any human-based assessment, this grading system is subjected to disparities — different pathologists can assign different grades to the same tumor, so patients may not be getting the most appropriate treatment. Researchers have tried to automate the process with digital slide scanners and image-processing methods. But with high-grade breast cancers, whose cells cluster and overlap, these methods have not worked well. With a little adaptation, the new method, previously used to detect damaged surface areas or underwater marine structures like bridge piers, off-shore turbine platforms, and pipelines, seems to have the potential to overcome those difficulties. To apply the method to histopathology images of breast cancer tissues, the researchers used an algorithm to detect nuclei boundaries, which allow
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