Input Data Adaptive Learning (IDAL) for Sub-acute Ischemic Stroke Lesion Segmentation
This addresses the issue of handling heterogeneous and sparsely annotated medical imaging data for sub-acute ischemic stroke lesion segmentation, which is incremental as it builds on existing large database training methods.
The paper tackles the problem of non-optimal classifiers in medical applications with highly variable pathologies by proposing Input Data Adaptive Learning (IDAL), which adaptively selects optimal training samples for given input data. The method was evaluated on the SISS challenge and led to a significant improvement in classification accuracy.
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.