Breast cancer is the most prevalent cancer among women globally, making early and accurate detection essential for effective treatment and improved survival rates. This paper presents a method designed to detect and localize breast cancer using deep learning, specifically convolutional neural networks. The approach classifies histological images of breast tissue as either tumor-positive or tumor-negative.
View Article and Find Full Text PDFThis paper presents a comprehensive study of hydrogenated amorphous silicon (a-Si)-based detectors, utilizing electrical characterization, Raman spectroscopy, photoemission, and inverse photoemission techniques. The unique properties of a-Si have sparked interest in its application for radiation detection in both physics and medicine. Although amorphous silicon (a-Si) is inherently a highly defective material, hydrogenation significantly reduces defect density, enabling its use in radiation detector devices.
View Article and Find Full Text PDFPregnancies ending before 26 weeks contribute 1% of births but 40% of infant deaths in the United States. The rate of these "periviable" births to non-Hispanic (NH) Black women exceeds four times that for NH whites. Small male periviable infants remain most likely to die.
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