Authors
Chen, Tung-I
Wu, Min-Sheng
Chang, Yu-Cheng
Lin, Jhih-Yuan
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Issue Date
2019
Publisher
University of Minnesota Libraries Publishing
Type
Article
Abstract
Since we are trying to deal with the medical images of real patients, the dataset are usually predominantly composed of ”normal” samples. The target classes only appear in a very small portion of the entire dataset, which leads to the so-called class imbalance problem. Besides, there is only a small percentage of foreground inside the ”abnormal” images. The great majority of background leads the significant detrimental effect on training. In such cases, model tends to focus on learning the dominant classes, leading to the poor prediction of minority class. However, the incorrect classification of pathological images can cause serious consequence in clinical practice.
Identifiers
doi: 10.24926/548719.065