BYR Achieve · 镜像论坛

在上一期AI文献推荐中，我们为您推荐了人工智能在医学研究领域的热点论文,包括在医学预测、医学筛查等方面的文献。本期我们为您选取了4篇文献，介绍AI在医学图像领域的热点文献，包括结合了边缘计算、基于区块链的对等网络和协调的去中心化分散机器学习Swarm Learning技术，可自动自配置的基于深度学习的图像分割方法，人工智能在心血管成像中的应用，利用深度学习通过眼底照片预测光学相干断层扫描成像得出的糖尿病黄斑水肿分级等文献，推送给相关领域的科研人员。文献一去中心化的分散机器学习Swarm Learning技术 Swarm Learning for decentralized and confidential clinical machine learning Warnat-Herresthal, Stefanie, etc. NATURE, 2021, 594(7862): 265-270 Fast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine. Patients with leukaemia can be identified using machine learning on the basis of their blood transcriptomes. However, there is an increasing divide between what is technically possible and what is allowed, because of privacy legislation. Here, to facilitate the integration of any medical data from any data owner worldwide without violating privacy laws, we introduce Swarm Learning-a decentralized machine-learning approach that unites edge computing, blockchain-based peer-to-peer networking and coordination while maintaining confidentiality without the need for a central coordinator, thereby going beyond federated learning. To illustrate the feasibility of using Swarm Learning to develop disease classifiers using distributed data, we chose four use cases of heterogeneous diseases (COVID-19, tuberculosis, leukaemia and lung pathologies). With more than 16,400 blood transcriptomes derived from 127 clinical studies with non-uniform distributions of cases and controls and substantial study biases, as well as more than 95,000 chest X-ray images, we show that Swarm Learning classifiers outperform those developed at individual sites. In addition, Swarm Learning completely fulfils local confidentiality regulations by design. We believe that this approach will notably accelerate the introduction of precision medicine. 阅读原文 https://www.nature.com/articles/s41586-021-03583-3 Concept of Swarm Learning 文献二基于深度学习的生物医学图像分割方法 nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation Isensee, Fabian, etc. NATURE METHODS, 2021, 18(2): 203–211 nnU-Net is a deep learning-based image segmentation method that automatically configures itself for diverse biological and medical image segmentation tasks. nnU-Net offers state-of-the-art performance as an out-of-the-box tool. Biomedical imaging is a driver of scientific discovery and a core component of medical care and is being stimulated by the field of deep learning. While semantic segmentation algorithms enable image analysis and quantification in many applications, the design of respective specialized solutions is non-trivial and highly dependent on dataset properties and hardware conditions. We developed nnU-Net, a deep learning-based segmentation method that automatically configures itself, including preprocessing, network architecture, training and post-processing for any new task. The key design choices in this process are modeled as a set of fixed parameters, interdependent rules and empirical decisions. Without manual intervention, nnU-Net surpasses most existing approaches, including highly specialized solutions on 23 public datasets used in international biomedical segmentation competitions. We make nnU-Net publicly available as an out-of-the-box tool, rendering state-of-the-art segmentation accessible to a broad audience by requiring neither expert knowledge nor computing resources beyond standard network training. 阅读原文 https://www.nature.com/articles/s41592-020-01008-z 文献三人工智能在心血管成像中的应用 Applications of artificial intelligence in cardiovascular imaging Sermesant, Maxime, etc. NATURE REVIEWS CARDIOLOGY, 2021 Research into artificial intelligence (AI) has made tremendous progress over the past decade. In particular, the AI-powered analysis of images and signals has reached human-level performance in many applications owing to the efficiency of modern machine learning methods, in particular deep learning using convolutional neural networks. Research into the application of AI to medical imaging is now very active, especially in the field of cardiovascular imaging because of the challenges associated with acquiring and analysing images of this dynamic organ. In this Review, we discuss the clinical questions in cardiovascular imaging that AI can be used to address and the principal methodological AI approaches that have been developed to solve the related image analysis problems. Some approaches are purely data-driven and rely mainly on statistical associations, whereas others integrate anatomical and physiological information through additional statistical, geometric and biophysical models of the human heart. In a structured manner, we provide representative examples of each of these approaches, with particular attention to the underlying computational imaging challenges. Finally, we discuss the remaining limitations of AI approaches in cardiovascular imaging (such as generalizability and explainability) and how they can be overcome. In this Review, Sermesant and colleagues discuss the applications of artificial intelligence (AI) in cardiovascular imaging and explain how pre-existing clinical knowledge can be included in AI methods to increase robustness. They also discuss the limitations of AI approaches in cardiovascular imaging and how they can be overcome. 阅读原文 https://www.nature.com/articles/s41569-021-00527-2 文献四利用眼底照片预测中心性糖尿病黄斑水肿的深度学习模型 Predicting optical coherence tomography-derived diabetic macular edema grades from fundus photographs using deep learning Varadarajan, Avinash V., etc. NATURE COMMUNICATIONS, 2020, 11(1) Center-involved diabetic macular edema (ci-DME) is a major cause of vision loss. Although the gold standard for diagnosis involves 3D imaging, 2D imaging by fundus photography is usually used in screening settings, resulting in high false-positive and false-negative calls. To address this, we train a deep learning model to predict ci-DME from fundus photographs, with an ROC-AUC of 0.89 (95% CI: 0.87-0.91), corresponding to 85% sensitivity at 80% specificity. In comparison, retinal specialists have similar sensitivities (82-85%), but only half the specificity (45-50%, p<0.001). Our model can also detect the presence of intraretinal fluid (AUC: 0.81; 95% CI: 0.81-0.86) and subretinal fluid (AUC 0.88; 95% CI: 0.85-0.91). Using deep learning to make predictions via simple 2D images without sophisticated 3D-imaging equipment and with better than specialist performance, has broad relevance to many other applications in medical imaging. 阅读原文 https://www.nature.com/articles/s41467-019-13922-8 The proposed ci-DME model

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