| Summary: | 碩士 === 國立成功大學 === 電腦與通信工程研究所 === 107 === Medical images segmentation is a fundamental challenge in medical image analysis. A major concern in the application of biomedical images in deep learning is insufficient number of annotated samples. Since the annotation process requires specialty-oriented knowledge and there are often too many instances in images (e.g. cells), this can incur a great deal of annotation effort and cost. Another concern is class imbalance problem, which is a critical obstacle commonly occurred in biomedical images. Considering the application of lymphocyte detection, an important lymphocyte subpopulation is extremely fewer than other cells, which would make training more biased toward the majority class. However, traditional labeling strategies, such as active learning, are ineffective in finding enough minority samples to train. Hence, this study deploys a low-cost method for manual annotation for efficiently lymphocyte detection in domains exhibiting extreme class imbalance. To address these problems, this paper proposed an active learning framework to reduce the total labeled workload while solving the extreme class imbalance problem by both under-sampling majority class and over-sampling minority class. Experimental results show that the proposed method can achieve annotation-effective solution in extremely imbalanced class segmentation. The contribution of the proposed method has three-fold, (1) we proposed an AL framework for solving the extreme class imbalance problem by both under-sampling majority class and over-sampling minority class. (2) the proposed framework achieves good performance for lymphocyte detection in histopathological image with fewer labeled samples. Finally, (3) quantitative analysis of lymphocytes is provided for more objective diagnosis.
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