Department of Computer Science, University of Georgia, Athens, GA 30602, USA.
School of Physics & Information Technology, Shaanxi Normal University, Xi'an 710119, China.
Zhong Nan Da Xue Xue Bao Yi Xue Ban. 2022 Aug 28;47(8):981-993. doi: 10.11817/j.issn.1672-7347.2022.220376.
Recent advancement in natural language processing (NLP) and medical imaging empowers the wide applicability of deep learning models. These developments have increased not only data understanding, but also knowledge of state-of-the-art architectures and their real-world potentials. Medical imaging researchers have recognized the limitations of only targeting images, as well as the importance of integrating multimodal inputs into medical image analysis. The lack of comprehensive surveys of the current literature, however, impedes the progress of this domain. Existing research perspectives, as well as the architectures, tasks, datasets, and performance measures examined in the present literature, are reviewed in this work, and we also provide a brief description of possible future directions in the field, aiming to provide researchers and healthcare professionals with a detailed summary of existing academic research and to provide rational insights to facilitate future research.
近年来，自然语言处理（NLP）和医学成像领域的进展使得深度学习模型得到了广泛的应用。这些进展不仅提高了对数据的理解，也让人们对最先进架构的知识及其在现实世界中的潜力有了更多的了解。医学成像研究人员已经认识到，仅针对图像的局限性，以及将多模态输入整合到医学图像分析中的重要性。然而，目前文献中缺乏对现有文献的全面调查，这阻碍了该领域的发展。在这项工作中，我们回顾了现有的研究视角，以及在现有文献中检查的架构、任务、数据集和性能度量，我们还简要描述了该领域未来可能的发展方向，旨在为研究人员和医疗保健专业人员提供现有学术研究的详细总结，并提供合理的见解，以促进未来的研究。
