Weakly Supervised Learning with Positive and Unlabeled Data for Automatic Brain Tumor Segmentation

Weakly Supervised Learning with Positive and Unlabeled Data for Automatic Brain Tumor Segmentation

A major obstacle to the learning-based segmentation of healthy and tumorous brain tissue is the requirement of having to create a fully labeled training dataset. Obtaining these data requires tedious and error-prone manual labeling with respect to both <i>tumor</i> and <i>non-tumor...

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Bibliographic Details
Main Authors:	Daniel Wolf, Sebastian Regnery, Rafal Tarnawski, Barbara Bobek-Billewicz, Joanna Polańska, Michael Götz
Format:	Article
Language:	English
Published:	MDPI AG 2022-10-01
Series:	Applied Sciences
Subjects:	image segmentation tumor segmentation machine learning random forests MRI PU-learning
Online Access:	https://www.mdpi.com/2076-3417/12/21/10763

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