Abstract
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.
Original language | English (US) |
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Title of host publication | Intravascular Imaging and Computer Assisted Stenting, and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis - 6th Joint International Workshops, CVII-STENT 2017 and 2nd International Workshop, LABELS 2017 Held in Conjunction with MICCAI 2017, Proceedings |
Editors | Tal Arbel, M. Jorge Cardoso |
Publisher | Springer Verlag |
Pages | 67-76 |
Number of pages | 10 |
ISBN (Print) | 9783319675336 |
DOIs | |
State | Published - 2017 |
Event | 6th Joint International Workshops on Computing and Visualization for Intravascular Imaging and Computer Assisted Stenting, CVII-STENT 2017 and 2nd International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, LABELS 2017 held in Conjunction with 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017 - Quebec City, Canada Duration: Sep 10 2017 → Sep 14 2017 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 10552 LNCS |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Other
Other | 6th Joint International Workshops on Computing and Visualization for Intravascular Imaging and Computer Assisted Stenting, CVII-STENT 2017 and 2nd International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, LABELS 2017 held in Conjunction with 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017 |
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Country/Territory | Canada |
City | Quebec City |
Period | 9/10/17 → 9/14/17 |
Bibliographical note
Publisher Copyright:© 2017, Springer International Publishing AG.
Keywords
- Image classification
- Real data augmentation
- Unbalanced data