Aljuaid Abeer, Anwar Mohd
Department of Computer Science, North Carolina A&T State University, 1601 E Market St, Greensboro, NC 27411 USA.
SN Comput Sci. 2022;3(4):292. doi: 10.1007/s42979-022-01166-1. Epub 2022 May 17.
Medical image interpretation is an essential task for the correct diagnosis of many diseases. Pathologists, radiologists, physicians, and researchers rely heavily on medical images to perform diagnoses and develop new treatments. However, manual medical image analysis is tedious and time consuming, making it necessary to identify accurate automated methods. Deep learning-especially supervised deep learning-shows impressive performance in the classification, detection, and segmentation of medical images and has proven comparable in ability to humans. This survey aims to help researchers and practitioners of medical image analysis understand the key concepts and algorithms of supervised learning techniques. Specifically, this survey explains the performance metrics of supervised learning methods; summarizes the available medical datasets; studies the state-of-the-art supervised learning architectures for medical imaging processing, including convolutional neural networks (CNNs) and their corresponding algorithms, region-based CNNs and their variants, fully convolutional networks (FCN) and U-Net architecture; and discusses the trends and challenges in the application of supervised learning methods to medical image analysis. Supervised learning requires large labeled datasets to learn and achieve good performance, and data augmentation, transfer learning, and dropout techniques have widely been employed in medical image processing to overcome the lack of such datasets.
医学图像解读是许多疾病正确诊断的一项重要任务。病理学家、放射科医生、内科医生和研究人员在很大程度上依赖医学图像来进行诊断和开发新的治疗方法。然而，手动医学图像分析既繁琐又耗时，因此有必要识别准确的自动化方法。深度学习——尤其是监督深度学习——在医学图像的分类、检测和分割方面表现出令人印象深刻的性能，并且已被证明在能力上可与人类相媲美。本综述旨在帮助医学图像分析的研究人员和从业人员理解监督学习技术的关键概念和算法。具体而言，本综述解释了监督学习方法的性能指标；总结了可用的医学数据集；研究了用于医学成像处理的最新监督学习架构，包括卷积神经网络（CNN）及其相应算法、基于区域的CNN及其变体、全卷积网络（FCN）和U-Net架构；并讨论了监督学习方法在医学图像分析应用中的趋势和挑战。监督学习需要大量带标签的数据集来进行学习并实现良好的性能，数据增强、迁移学习和随机失活技术已广泛应用于医学图像处理中，以克服此类数据集的不足。
