医学图像非结构大数据实现分割诊断的机器学习方法

A machine learning method for segmentation and diagnosis of medical image unstructured big data

随着人工智能技术的发展与应用和医学图像数据爆炸式增长,传统依靠医生对医学图像进行人工分割诊断,不仅工作效率低下、工作量大,还容易产生误诊、漏诊。机器学习,尤其是深度学习在医学图像非结构化大数据领域发挥着越来越重要的作用。为了进一步了解机器学习在医学图像自动分割和识别诊断中的研究,本文对机器学习及其在医学图像分析领域的研究进展进行综述,为机器学习方法解决医学图像非结构化大数据提供方法学参考。

Under the background of explosive growth of medical image data,traditionally medical image segmentation diagnosis relying on doctors manually became inefficient and hard work and was also prone to misdiagnosis and missed diagnosis.The machine learning,especially deep learning,plays an increasingly important role in the field of medical image unstructured big data,with the development and application of artificial intelligence technology.In order to further understand the research of machine learning in the automatic segmentation and recognition of medical images,this paper reviewed the progress of machine learning and its research in the field of medical image analysis,providing useful method ological reference for application of machine learning method in solving unstructured big data of medical images.