Deep learning based digital cell profiles for risk stratification of urine cytology images

Urine cytology is a test for the detection of high‐grade bladder cancer. In clinical practice, the pathologist would manually scan the sample under the microscope to locate atypical and malignant cells. They would assess the morphology of these cells to make a diagnosis. Accurate identification of a...

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Vydané v:	Cytometry. Part A Ročník 99; číslo 7; s. 732 - 742
Hlavní autori:	Awan, Ruqayya, Benes, Ksenija, Azam, Ayesha, Song, Tzu‐Hsi, Shaban, Muhammad, Verrill, Clare, Tsang, Yee Wah, Snead, David, Minhas, Fayyaz, Rajpoot, Nasir
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Hoboken, USA John Wiley & Sons, Inc 01.07.2021 Wiley Subscription Services, Inc
Predmet:	Annotations Bladder cell classification cell detection cell segmentation Cellular biology Cytodiagnosis Cytology Deep Learning Depth profiling Diagnosis Digital imaging digital risk Histopathology Machine learning Malignancy Medical imaging Morphology oversampling Risk Risk Assessment ROC Curve The Paris System Urine urine cytology The Paris System deep learning urine cytology cell classification cell segmentation cell detection digital risk oversampling
ISSN:	1552-4922, 1552-4930, 1552-4930
On-line prístup:	Získať plný text
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