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 tumor and non-tumor areas. To mitigate this pro...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article (Journal) |
| Language: | English |
| Published: |
24 October 2022
|
| In: |
Applied Sciences
Year: 2022, Volume: 12, Issue: 21, Pages: 1-14 |
| ISSN: | 2076-3417 |
| DOI: | 10.3390/app122110763 |
| Online Access: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.3390/app122110763 Verlag, lizenzpflichtig, Volltext: https://www.mdpi.com/2076-3417/12/21/10763 
|
| Author Notes: | Daniel Wolf, Sebastian Regnery, Rafal Tarnawski, Barbara Bobek-Billewicz, Joanna Polańska and Michael Götz |
| Summary: | 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 tumor and non-tumor areas. To mitigate this problem, we propose a new method to obtain high-quality classifiers from a dataset with only small parts of labeled tumor areas. This is achieved by using positive and unlabeled learning in conjunction with a domain adaptation technique. The proposed approach leverages the tumor volume, and we show that it can be either derived with simple measures or completely automatic with a proposed estimation method. While learning from sparse samples allows reducing the necessary annotation time from 4 h to 5 min, we show that the proposed approach further reduces the necessary annotation by roughly 50% while maintaining comparative accuracies compared to traditionally trained classifiers with this approach. |
|---|---|
| Item Description: | Gesehen am 20.01.2023 |
| Physical Description: | Online Resource |
| ISSN: | 2076-3417 |
| DOI: | 10.3390/app122110763 |