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Real Data Augmentation for Medical Image Classification by Chuanhai Zhang; Wallapak Tavanapong; Johnny Wong; Piet C. de Groen; JungHwan Oh is a book available to read on EtoBox.

What is Real Data Augmentation for Medical Image Classification about?

Many medical image classification tasks share a common unbalanced data problem. That is images of the target classes, e.g., certain types of diseases, only appear in a very small portion of the entire dataset. Nowadays, large collections of medical images are readily available. However, it is costly and may not even be feasible for medical experts to manually comb through a huge unlabeled dataset to obtain enough representative examples of the rare classes. In this paper, we propose a new method called Unified LF&SM to recommend most similar images for each class from a large unlabeled dataset for verification by medical experts and inclusion in the seed labeled dataset. Our real data augmentation significantly reduces expensive manual labeling time. In our experiments, Unified LF&SM performed best, selecting a high percentage of relevant images in its recommendation and achieving the best classification accuracy. It is easily extendable to other medical image classification problems.

Author

Chuanhai Zhang; Wallapak Tavanapong; Johnny Wong; Piet C. de Groen; JungHwan Oh

Publisher

Springer International Publishing : Imprint: Springer

Published

2017

Language

EN

ISBN

9783319675336

Subjects

Computer Science, Engineering, Mathematics