| Autor: |
Götz, Michael, Weber, Christian, Kolb, Christoph, Maier-Hein, Klaus |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2015 |
| Druh dokumentu: |
Working Paper |
| DOI: |
10.1007/978-3-319-30858-6_25 |
| Popis: |
In machine learning larger databases are usually associated with higher classification accuracy due to better generalization. This generalization may lead to non-optimal classifiers in some medical applications with highly variable expressions of pathologies. This paper presents a method for learning from a large training base by adaptively selecting optimal training samples for given input data. In this way heterogeneous databases are supported two-fold. First, by being able to deal with sparsely annotated data allows a quick inclusion of new data set and second, by training an input-dependent classifier. The proposed approach is evaluated using the SISS challenge. The proposed algorithm leads to a significant improvement of the classification accuracy. |
| Databáze: |
arXiv |
| Externí odkaz: |
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