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<div><p>Digital pathology enables automatic analysis of histopathological sections using artificial intelligence. Automatic evaluation could improve diagnostic efficiency and find associations between morphological features and clinical outcome. For development of such prediction models...

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محفوظ في:

التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Maren Høibø (21507680) (author)
مؤلفون آخرون:	André Pedersen (12005618) (author), Vibeke Grotnes Dale (21736148) (author), Sissel Marie Berget (21736151) (author), Borgny Ytterhus (5567993) (author), Cecilia Lindskog (400359) (author), Elisabeth Wik (106776) (author), Lars A. Akslen (7443932) (author), Ingerid Reinertsen (6316424) (author), Erik Smistad (836617) (author), Marit Valla (5567984) (author)
منشور في:	2025
الموضوعات:	Biophysics Biochemistry Medicine Cell Biology Immunology Developmental Biology Cancer Space Science whole slide images second test set div >< p attention gated u tissue microarray cores pathologists ’ annotations mean dice scores breast cancer slides invasive epithelial cells benign epithelial cells immunohistochemistry guided segmentation epithelial cells breast cancer tissue microarrays invasive epithelium two patients situ </ qualitative scoring morphological features find associations fifth cohort cytokeratin ae1 clinical outcome best results accurate subclassification 839 patients 21 patients
الوسوم:	إضافة وسم لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!

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