Name
Abstract | ||
|---|---|---|
Reviewing videos from medical procedures is a tedious work that requires concentration for extended hours and usually screens thousands of frames to find only a few positive cases that indicate probable presence of disease. Computational classification algorithms are sought to automate the reviewing process. The class imbalance problem becomes challenging when the learning process is driven by relative few minority class samples. The learning algorithms using imbalanced data sets generally result in large number of false negatives. In this article, we present an efficient rebalancing method for finding video frames that contain bleeding lesions. The majority class generally has clusters of data within them. Here we cluster the majority class and under-sample the each cluster based on its variance so that useful examples would not be lost during the under-sampling process. The balance of bleeding to non-bleeding frames is restored by the proposed cluster-based under-sampling and over-sampling using Synthetic Minority Over-sampling Technique (SMOTE). Experiments were conducted using synthetic data and videos manually annotated by medical specialists for obscure bleeding detection. Our method achieved a high average sensitivity and specificity. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1117/12.843944 | Proceedings of SPIE |
Keywords | Field | DocType |
sampling technique,video,synthetic data,algorithms | Training set,Data set,Pattern recognition,Oversampling,Computer science,Synthetic data,Artificial intelligence,Statistical classification,Resampling,Machine learning | Conference |
Volume | ISSN | Citations |
7624 | 0277-786X | 1 |
PageRank | References | Authors |
0.37 | 7 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Balathasan Giritharan | 1 | 4 | 1.43 |
| Xiao-Hui Yuan | 2 | 534 | 75.44 |