Research on ovarian cancer pathological image recognition technology based on deep learning
Recent years, with the development of computer hardware technology, deep learning has made great progress in the fields of computer vision, natural language processing, machine translation, medical imaging and bioinformatics. The use of deep learning, especially supervised learning for pathological image analysis has also become a hot issue in the field of medical diagnosis. Ovarian cancer is the fourth most common cause of death among women in the world. Effective diagnosis of ovarian cancer can greatly increase the success rate of later treatment, and its pathological image-based diagnosis is regarded as the “gold standard” in most cancer diagnosis. However, the large amount of data and the complexity of the image make the diagnosis process take a long time. The serious lack of pathologists in China increases the burden on existing pathologists, which can easily cause fatigue and distraction, and affect the accuracy of diagnosis. Therefore, the research of ovarian cancer pathological image technology based on deep learning is particularly important.
Using deep learning, we can automatically extract abstract and advanced feature expressions in pathological images, and then analyze their advanced features. In addition, weakly supervised learning technology also provides a theoretical basis for using only data containing slide-level annotations. This paper aims to study the automatic analysis method of ovarian cancer pathological image based on deep learning. We build an independent database of ovarian cancer pathological images, train and optimize the model, and implement deep learning-based auxiliary diagnosis of ovarian cancer.
The main innovations and contributions of this article are summarized as follows: