Department of Computer Science
In this paper, we describe our model designed for automatic detection of diseases based on multimedia data collected in hospitals. Specifically, a two-stage learning strategy is designed to predict the diseases. In the first stage, a dimensionality reduction method called bidirectional marginal Fisher analysis (BMFA) is proposed to project the original data to the low-dimensional space, with the key discriminant information being well preserved. In the second stage, the multi-class support vector machine (SVM) is utilized on the low-dimensional space for detection. Experimental results demonstrate the efficiency of designed model.
Source Publication Title
Gravier, Guillaume ; Bischke, Benjamin ; Demarty, Claire-Hélène ; Zaharieva, Maia ; Riegler, Michael ; Dellandrea, Emmanuel ; Bogdanov, Dmitry ; Sutcliffe, Richard ; Jones, Gareth J.F. ; Larson, Martha
Copyright © held by the owner/author(s)
This work was supported in part by the National Natural Science Foundation of China under Grant 61503317, and in part by the Faculty Research Grant of Hong Kong Baptist University (HKBU) under Project FRG2/16-17/032.
Link to Publisher's Edition
Liu, Yang, Zhonglei Gu, and William K. Cheung. "HKBU at MediaEval 2017 Medico: Medical multimedia task." MediaEval 2017 (2017).