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	Author	Frederic Sampedro; Sergio Escalera; Anna Puig
	Title	Iterative Multiclass Multiscale Stacked Sequential Learning: definition and application to medical volume segmentation			Type	Journal Article
	Year	2014	Publication	Pattern Recognition Letters	Abbreviated Journal	PRL
	Volume	46	Issue		Pages	1-10
	Keywords	Machine learning; Sequential learning; Multi-class problems; Contextual learning; Medical volume segmentation
	Abstract	In this work we present the iterative multi-class multi-scale stacked sequential learning framework (IMMSSL), a novel learning scheme that is particularly suited for medical volume segmentation applications. This model exploits the inherent voxel contextual information of the structures of interest in order to improve its segmentation performance results. Without any feature set or learning algorithm prior assumption, the proposed scheme directly seeks to learn the contextual properties of a region from the predicted classifications of previous classifiers within an iterative scheme. Performance results regarding segmentation accuracy in three two-class and multi-class medical volume datasets show a significant improvement with respect to state of the art alternatives. Due to its easiness of implementation and its independence of feature space and learning algorithm, the presented machine learning framework could be taken into consideration as a first choice in complex volume segmentation scenarios.
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	Area		Expedition		Conference
	Notes	HuPBA;MILAB			Approved	no
	Call Number	Admin @ si @ SEP2014			Serial	2550
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