Generalized Fixation Invariant Nuclei Detection through Domain Adaptation Based Deep Learning

Nucleus detection is a fundamental task in histological image analysis and an important tool for many follow up analyses. It is known that sample preparation and scanning procedure of histological slides introduce a great amount of variability to the histological images and poses challenges for auto...

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Bibliographic Details
Main Authors:	Bova, G.S (Author), Hognas, G. (Author), Ruusuvuori, P. (Author), Valkonen, M. (Author)
Format:	Article
Language:	English
Published:	Institute of Electrical and Electronics Engineers Inc. 2021
Subjects:	accuracy algorithm Article artificial neural network cancer grading cell nucleus Cell Nucleus colonoscopy computer assisted tomography computer language computer model controlled study convolutional neural network Convolutional neural networks decision tree deep learning Deep learning Deep Learning Detection accuracy detection algorithm digital pathology domain adaptation Domain adaptation electroencephalography eosin follow up Follow up formaldehyde formalin-fixed frozen section gray matter hematoxylin histogram Histological images Histological Techniques histology histopathology human human tissue Humans image analysis image processing Image Processing, Computer-Assisted image quality image reconstruction image segmentation immunohistochemistry knee learning machine learning nerve cell network Neural Networks, Computer nuclei detection Nuclei detections PAXgene-fixed predictive value prostatectomy Sample preparation support vector machine tissue fixation training Training data validation process white matter workflow
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Generalized Fixation Invariant Nuclei Detection through Domain Adaptation Based Deep Learning

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