Input Data Adaptive Learning (IDAL) for Sub-acute Ischemic Stroke Lesion
  Segmentation
Michael Götz Christian Weber Christoph Kolb Klaus Maier-Hein
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.