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학술대회 Tournament Based Ranking CNN for the Cataract Grading
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저자
김도현, 전태준, 엄영섭, 김채리, 김대영
발행일
201907
출처
International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) 2019, pp.1630-1636
DOI
https://dx.doi.org/10.1109/EMBC.2019.8856636
협약과제
19HS1500, 심혈관질환을 위한 인공지능 주치의 기술 개발, 김승환
초록
Solving the classification problem, unbalanced number of dataset among the classes often causes performance degradation. Especially when some classes dominate the other classes with its large number of datasets, trained model shows low performance in identifying the dominated classes. This is common case when it comes to medical dataset. Because the case with a serious degree is not quite usual, there are imbalance in number of dataset between severe case and normal cases of diseases. Also, there is difficulty in precisely identifying grade of medical data because of vagueness between them. To solve these problems, we propose new architecture of convolutional neural network named Tournament based Ranking CNN which shows remarkable performance gain in identifying dominated classes while trading off very small accuracy loss in dominating classes. Our Approach complemented problems that occur when method of Ranking CNN that aggregates outputs of multiple binary neural network models is applied to medical data. By having tournament structure in aggregating method and using very deep pretrained binary models, our proposed model recorded 68.36% of exact match accuracy, while Ranking CNN recorded 53.40%, pretrained Resnet recorded 56.12% and CNN with linear regression recorded 57.48%. As a result, our proposed method is applied efficiently to cataract grading which have ordinal labels with imbalanced number of data among classes, also can be applied further to medical problems which have similar features to cataract and similar dataset configuration.
KSP 제안 키워드
Accuracy loss, Classification problems, Convolution neural network(CNN), Exact match, Linear regression, Medical dataset, Performance gain, Proposed model, neural network model, performance degradation