图像分割技术类别失衡问题的研究

Research on the Imbalanced Categories of Image Segmentation Technology

医学图像分割作为医疗诊断与治疗规划的核心技术,对精准医疗至关重要。针对医学图像中的类别不平衡问题,文章提出了一种基于卷积神经网络(Convolutional Neural Networks,CNN)的图像分割技术,并提出一种新算法,利用PyTorch深度学习框架,深入探索了U-Net、FCN和DeepLab等变体,为疾病的早期诊断、个性化治疗设计以及病情监测提供了强有力的技术支持。

As the core technology of medical diagnosis and treatment planning,medical image segmentation is very important for precision medicine.In order to solve the problem of category imbalance in medical images,this paper proposes a Convolutional Neural Networks(CNN)based image segmentation technique,and proposes a novel algorithm using PyTorch deep learning framework.And explore the U-Net,FCN and DeepLab variants,designed for early diagnosis,personalized treatment disease and illness monitoring provides a strong technical support.