TY  - JOUR
AU  - Frederic Sampedro
AU  - Sergio Escalera
PY  - 2015//
TI  - Spatial codification of label predictions in Multi-scale Stacked Sequential Learning: A case study on multi-class medical volume segmentation
T2  - IETCV
JO  - IET Computer Vision
SP  - 439
EP  - 446
VL  - 9
IS  - 3
N2  - In this study, the authors propose the spatial codification of label predictions within the multi-scale stacked sequential learning (MSSL) framework, a successful learning scheme to deal with non-independent identically distributed data entries. After providing a motivation for this objective, they describe its theoretical framework based on the introduction of the blurred shape model as a smart descriptor to codify the spatial distribution of the predicted labels and define the new extended feature set for the second stacked classifier. They then particularise this scheme to be applied in volume segmentation applications. Finally, they test the implementation of the proposed framework in two medical volume segmentation datasets, obtaining significant performance improvements (with a 95% of confidence) in comparison to standard Adaboost classifier and classical MSSL approaches.
SN  - 1751-9632
UR  - http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7108369&filter%3DAND%28p_IS_Number%3A7108333%29
L1  - http://refbase.cvc.uab.es/files/SaE2014.pdf
UR  - http://dx.doi.org/10.1049/iet-cvi.2014.0067
N1  - HuPBA;MILAB
ID  - Frederic Sampedro2015
ER  -