Improving Generalization with Active Learning
Machine Learning - Special issue on structured connectionist systems
Machine Learning
Segmentation of multispectral remote sensing images using active support vector machines
Pattern Recognition Letters
Semisupervised SVM batch mode active learning with applications to image retrieval
ACM Transactions on Information Systems (TOIS)
Computational Intelligence Based on Lattice Theory
Computational Intelligence Based on Lattice Theory
Hybrid deformable model for aneurysm segmentation
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Spatial decision forests for MS lesion segmentation in multi-channel MR images
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part I
Editorial: Special issue: Information engineering applications based on lattices
Information Sciences: an International Journal
IPMI'11 Proceedings of the 22nd international conference on Information processing in medical imaging
MLMI'11 Proceedings of the Second international conference on Machine learning in medical imaging
Knowledge management in image-based analysis of blood vessel structures
Knowledge and Information Systems
A cluster-assumption based batch mode active learning technique
Pattern Recognition Letters
Lattice algebra approach to single-neuron computation
IEEE Transactions on Neural Networks
Pattern Recognition Letters
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We perform the segmentation of medical images following an Active Learning approach that allows quick interactive segmentation minimizing the requirements for intervention of the human operator. The basic classifier is the Bootstrapped Dendritic Classifier (BDC), which combine the output of an ensemble of weak Dendritic Classifiers by majority voting. Weak Dendritic Classifiers are trained on bootstrapped samples of the train data setting a limit on the number of dendrites. We validate the approach on the segmentation of the thrombus in 3D Computed Tomography Angiography (CTA) data of Abdominal Aortic Aneurysm (AAA) patients simulating the human oracle by the provided ground truth. The generalization results in terms of accuracy and true positive ratio of the classification of the entire volume by the classifier trained on one slice confirm that the approach is worth its consideration for clinical practice.