Input Data Adaptive Learning (IDAL) for Sub-acute Ischemic Stroke Lesion Segmentation

• URL: http://arxiv.org/abs/2403.07428v1
• Date: Tue, 12 Mar 2024 09:11:02 GMT
• Title: Input Data Adaptive Learning (IDAL) for Sub-acute Ischemic Stroke Lesion Segmentation
• Authors: Michael G\"otz, Christian Weber, Christoph Kolb, Klaus Maier-Hein
• Abstract summary: This paper presents a method for learning from a large training base by adaptively selecting optimal training samples for given input data. The proposed algorithm leads to a significant improvement of the classification accuracy.
• Score: 0.11976120407592658
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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