A review of intelligent diagnosis methods of imaging gland cancer based on machine learning

Background

Gland cancer is a high-incidence disease endangering human health, and its early detection and treatment need efficient, accurate and objective intelligent diagnosis methods. In recent years, the advent of machine learning techniques has yielded satisfactory results in the intelligent gland cancer diagnosis based on clinical images, greatly improving the accuracy and efficiency of medical image interpretation while reducing the workload of doctors. The foci of this paper is to review, classify and analyze the intelligent diagnosis methods of imaging gland cancer based on machine learning and deep learning. To start with, the paper presents a brief introduction about some basic imaging principles of multi-modal medical images, such as the commonly used CT, MRI, US, PET, and pathology. In addition, the intelligent diagnosis methods of imaging gland cancer are further classified into supervised learning and weakly-supervised learning. Supervised learning consists of traditional machine learning methods like KNN, SVM, multilayer perceptron, etc. and deep learning methods evolving from CNN, meanwhile, weakly-supervised learning can be further categorized into active learning, semi-supervised learning and transfer learning. The state-of-the-art methods are illustrated with implementation details, including image segmentation, feature extraction, the optimization of classifiers, and their performances are evaluated through indicators like accuracy, precision and sensitivity. To conclude, the challenges and development trend of intelligent diagnosis methods of imaging gland cancer are addressed and discussed.