TY  - CONF
A1  - Khan, F.U.
A1  - Aziz, I.B.A.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85080867499&doi=10.1109%2fICBDA47563.2019.8987171&partnerID=40&md5=9db3bed50c6f2fcb65821d9fcc009ee2
EP  - 50
Y1  - 2019///
PB  - Institute of Electrical and Electronics Engineers Inc.
SN  - 9781728133089
N1  - cited By 1; Conference of 2019 IEEE Conference on Big Data and Analytics, ICBDA 2019 ; Conference Date: 19 November 2019 Through 21 November 2019; Conference Code:157670
N2  - In general, there are two open challenges for domain specific visual concept detection. First is the high intra-class variations and second is to collect large collection of sample training data covering the wide variety. In this research we present a novel medical image sampling approach to handle these two challenges. For huge intra-class variations present in the data collection we propose a unique clustering method to group similar data samples. We propose to measure similarity on the basis of membership degree between candidate groups. At the same time we handled the issue of large data collection over wide variety. We synthetically re-sampled the data on the basis of membership score. The membership score helped to make use of minor groups which has small sample size. Experiments show that our proposed method can achieve promising results and outperforms existing approaches particularly for medical image concept detection. © 2019 IEEE.
SP  - 45
TI  - Reducing high variability in medical image collection by a novel cluster based synthetic oversampling technique
ID  - scholars11137
KW  - Big data; Medical imaging
KW  -  Clustering; Clustering methods; Concept detection; Data sampling; Intra-class variation; Membership degrees; Oversampling technique; Visual concept detections
KW  -  Data acquisition
AV  - none
ER  -