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...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	Cytometry. Part A Ročník 99; číslo 7; s. 732 - 742
Hlavní autoři:	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:	angličtina
Vydáno:	Hoboken, USA John Wiley & Sons, Inc 01.07.2021 Wiley Subscription Services, Inc
Témata:	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 přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Buďte první, kdo okomentuje tento záznam!