Instance Segementation in Medical Images Using Weak Annotation, M.Sc. Thesis Sharif University of Technology ; Behroozi, Hamid (Supervisor) ; Mohammadzadeh, Nargesol Hoda (Co-Supervisor)
Abstract
Recent approaches in the field of semantic image segmentation rely on deep networks that are trained by pixel-level labels. This level of labeling requires a lot of time for the labeler person; because these networks require large training datasets to achieve optimal accuracy and the lack of data at the labeled pixel level causes a significant drop in their performance. In order to overcome this problem, weakly supervised segmentation approaches have been proposed. In these approaches, weaker labels such as image-level labels, bounding boxes, scribbles, etc. have been introduced to train the networks.In this thesis, a method for segmentation of kidney and kidney tumor in CT scan images based...
Cataloging briefInstance Segementation in Medical Images Using Weak Annotation, M.Sc. Thesis Sharif University of Technology ; Behroozi, Hamid (Supervisor) ; Mohammadzadeh, Nargesol Hoda (Co-Supervisor)
Abstract
Recent approaches in the field of semantic image segmentation rely on deep networks that are trained by pixel-level labels. This level of labeling requires a lot of time for the labeler person; because these networks require large training datasets to achieve optimal accuracy and the lack of data at the labeled pixel level causes a significant drop in their performance. In order to overcome this problem, weakly supervised segmentation approaches have been proposed. In these approaches, weaker labels such as image-level labels, bounding boxes, scribbles, etc. have been introduced to train the networks.In this thesis, a method for segmentation of kidney and kidney tumor in CT scan images based...
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