복부 CT 영상에서 심층 합성곱 신경망 기반의 국소 간 병변 분류를 위한 데이터 증강 기법의 효과 분석
Effect of Data Augmentation Techniques for Classification of Focal Liver Lesions Based on Deep Convolutional Neural Networks in Abdominal CT Images
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초록
논문에서는 복부 CT 영상에서 국소 간 병변 분류를 위한 심층 합성곱 신경망 학습에 다양한 데이터 증강 기법을 적용하여 의료 영상에서의 효과를 분석한다. 이를 위해 어파인 변환 기반, StyleGAN, Mixup, Augmix 기반 데이터 증강 기법을 각각 VGG16 심층 합성곱 신경망에 적용하여 국소 간 병변을 낭종, 혈관종, 전이암으로 분류하는 학습을 수행한다. 실험을 위해 각 데이터 증강 기법에 의해 훈련된 모델의 분류 결과에 대하여 정확도, 민감도, 특이도 분석을 통해 정량적 평가를 진행하고, 증강 영상 예시 분석과 tSNE 분석을 통해 정성적 평가를 수행하여 데이터 증강 기법의 효과를 분석한다.
Abstract
In this paper, we analyze the effects in medical images by applying various data augmentation techniques to deep convolutional neural network learning for classifying focal liver lesions in abdominal CT images. We apply affine transformation-based, StyleGAN, Mixup, and Augmixbased data augmentation techniques to the VGG16 convolutional neural network, respectively, to learn to classify local liver lesions into cysts, hemangiomas, and metastases. For the experiments, we validate and analyze the effect of the data augmentation through both a quantitative assessment by comparing accuracy, sensitivity, and specificity for classification results of models trained by each data augmentation technique and a qualitative assessment by observing augmented image examples and the tSNE feature distributions.
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