One-class classification for highly imbalanced medical image data
Long Gao, Lü Yang, Dooman Arefan, Shandong Wu · 2020
Computer-aided diagnosis plays an important role in clinical image diagnosis. Current clinical image classification tasks usually focus on binary classification, which need to collect samples for both the positive and negative classes in order to train a binary classifier. However, in many clinical scenarios, there may have many more samples in one class than in the other class, which results in the problem of data imbalance. Data imbalance is a severe problem that can substantially influence the performance of binary-class machine learning models. To address this issue, one-class classification, which focuses on learning features from the samples of one given class, has been proposed. In this work, we assess the one-class support vector machine (OCSVM) to solve the classification tasks on two highly imbalanced datasets, namely, space-occupying kidney lesions (including renal cell carcinoma and benign) data and breast cancer distant metastasis/non-metastasis imaging data. Experimental results show that the OCSVM exhibits promising performance compared to binary-class and other one-class classification methods.