Train, Learn, Expand, Repeat

• URL: http://arxiv.org/abs/2003.08469v2
• Date: Sun, 19 Apr 2020 12:25:11 GMT
• Title: Train, Learn, Expand, Repeat
• Authors: Abhijeet Parida, Aadhithya Sankar, Rami Eisawy, Tom Finck, Benedikt Wiestler, Franz Pfister, Julia Moosbauer
• Abstract summary: High-quality labeled data is essential to successfully train supervised machine learning models. Medical professionals who can expertly label the data are a scarce and expensive resource. We apply this technique on the segmentation of intracranial hemorrhage (ICH) in CT scans of the brain.
• Score: 0.15833270109954134
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: High-quality labeled data is essential to successfully train supervised machine learning models. Although a large amount of unlabeled data is present in the medical domain, labeling poses a major challenge: medical professionals who can expertly label the data are a scarce and expensive resource. Making matters worse, voxel-wise delineation of data (e.g. for segmentation tasks) is tedious and suffers from high inter-rater variance, thus dramatically limiting available training data. We propose a recursive training strategy to perform the task of semantic segmentation given only very few training samples with pixel-level annotations. We expand on this small training set having cheaper image-level annotations using a recursive training strategy. We apply this technique on the segmentation of intracranial hemorrhage (ICH) in CT (computed tomography) scans of the brain, where typically few annotated data is available.

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