2015 | OriginalPaper | Buchkapitel
Multi-source Information Gain for Random Forest: An Application to CT Image Prediction from MRI Data
verfasst von : Tri Huynh, Yaozong Gao, Jiayin Kang, Li Wang, Pei Zhang, Dinggang Shen, Alzheimer’s Disease Neuroimaging Initiative (ADNI)
Erschienen in: Machine Learning in Medical Imaging
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Random forest has been widely recognized as one of the most powerful learning-based predictors in literature, with a broad range of applications in medical imaging. Notable efforts have been focused on enhancing the algorithm in multiple facets. In this paper, we present an original concept of
multi-source information gain
that escapes from the conventional notion inherent to random forest. We propose the idea of characterizing information gain in the training process by utilizing
multiple beneficial sources of information
, instead of the
sole governing of prediction targets
as conventionally known. We suggest the use of location and input image patches as the secondary sources of information for guiding the splitting process in random forest, and experiment on the challenging task of predicting CT images from MRI data. The experimentation is thoroughly analyzed in two datasets, i.e., human brain and prostate, with its performance further validated with the integration of auto-context model. Results prove that the
multi-source information gain
concept effectively helps better guide the training process with consistent improvement in prediction accuracy.