Input Data Adaptive Learning (IDAL) for Sub-acute Ischemic Stroke Lesion Segmentation
<|reference_start|>Input Data Adaptive Learning (IDAL) for Sub-acute Ischemic Stroke Lesion Segmentation: 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.<|reference_end|>
@article{götz2024input,
    title={Input Data Adaptive Learning (IDAL) for Sub-acute Ischemic Stroke Lesion
  Segmentation},
    author={Michael G"otz, Christian Weber, Christoph Kolb, Klaus Maier-Hein},
    journal={Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic
  Brain Injuries. BrainLes 2015},
    year={2024},
    doi={10.1007/978-3-319-30858-6_25},
    archivePrefix={arXiv},
    eprint={2403.07428},
    primaryClass={eess.IV cs.CV}
}