Stain Normalization of Hematology Slides

dc.contributor.author	MUHAMMAD MUNEEB ARSHAD, Supervised by DR HASAN SAJID
dc.date.accessioned	2022-02-03T05:28:58Z
dc.date.available	2022-02-03T05:28:58Z
dc.date.issued	2021
dc.identifier.uri	http://10.250.8.41:8080/xmlui/handle/123456789/28570
dc.description.abstract	Deep learning for pathological examination is a booming trend in the current times. Many studies have been conducted to solve pathological problems using deep learning. However, since the properties of an acquired image vary with the acquisition equipment, a deep learning model would fail if the variance of an unseen image is different from that of the images in the training data. This study aims to find a method that transfers the training set variance on unseen images and helps to make the model inference more robust.	en_US
dc.language.iso	en_US	en_US
dc.publisher	SMME	en_US
dc.relation.ispartofseries	SMME-TH-681;
dc.subject	Deep learning, Object Detection, Neural Color Transfer, Cell Morphology, Stain Invariance	en_US
dc.title	Stain Normalization of Hematology Slides	en_US
dc.type	Thesis	en_US