热点文献带您关注AI技术的最新进展——图书馆前沿文献专题推荐服务（39）

2021-05-10

 

        在上一期AI文献推荐中，我们为您推荐了人工智能与边缘计算的热点论文,包括边缘计算与AI设备、深度强化学习、深度神经网络推理框架相结合等方面的文献。
本期我们为您选取了4篇文献，介绍人工智能与三维全息成像技术与重建、运动识别等方面的最新动态，包括利用三维卷积神经网络，基于视频评估逐搏心脏功能；基于深度学习的计算机生成全息技术，从RGB深度图像实时合成一个真实感的彩色三维全息图；三维卷积神经网络在头颈部血管造影血管快速分割与重建中的应用；基于大规模RGB+D动作识别数据集，利用深度学习方法进行3D行为识别等文献，推送给相关领域的科研人员。

文献一 利用三维卷积神经网络，基于视频评估逐搏心脏功能
Video-based AI for beat-to-beat assessment of cardiac function
Ouyang, David, etc.
NATURE, 2020, 580(7802): 252-256
Accurate assessment of cardiac function is crucial for the diagnosis of cardiovascular disease, screening for cardiotoxicity and decisions regarding the clinical management of patients with a critical illness. However, human assessment of cardiac function focuses on a limited sampling of cardiac cycles and has considerable inter-observer variability despite years of training. Here, to overcome this challenge, we present a video-based deep learning algorithm-EchoNet-Dynamic-that surpasses the performance of human experts in the critical tasks of segmenting the left ventricle, estimating ejection fraction and assessing cardiomyopathy y. Trained on echocardiogram videos, our model accurately segments the left ventricle with a Dice similarity coefficient of 0.92, predicts ejection fraction with a mean absolute error of 4.1% and reliably classifies heart failure with reduced ejection fraction (area under the curve of 0.97). In an external dataset from another healthcare system, EchoNet-Dynamic predicts the ejection fraction with a mean absolute error of 6.0% and classifies heart failure with reduced ejection fraction with an area under the curve of 0.96. Prospective evaluation with repeated human measurements confirms that the model has variance that is comparable to or less than that of human experts. By leveraging information across multiple cardiac cycles, our model can rapidly identify subtle changes in ejection fraction, is more reproducible than human evaluation and lays the foundation for precise diagnosis of cardiovascular disease in real time. As a resource to promote further innovation, we also make publicly available a large dataset of 10,030 annotated echocardiogram videos.
阅读原文 https://www.nature.com/articles/s41586-020-2145-8#article-info


文献二 基于卷积神经网络的实时三维全息图合成
Towards real-time photorealistic 3D holography with deep neural networks
Shi, Liang, etc.
NATURE, 2021, 591(7849): 234-239
The ability to present three-dimensional (3D) scenes with continuous depth sensation has a profound impact on virtual and augmented reality, human-computer interaction, education and training. Computer-generated holography (CGH) enables high-spatio-angular-resolution 3D projection via numerical simulation of diffraction and interference. Yet, existing physically based methods fail to produce holograms with both per-pixel focal control and accurate occlusion. The computationally taxing Fresnel diffraction simulation further places an explicit trade-off between image quality and runtime, making dynamic holography impractical. Here we demonstrate a deep-learning-based CGH pipeline capable of synthesizing a photorealistic colour 3D hologram from a single RGB-depth image in real time. Our convolutional neural network (CNN) is extremely memory efficient (below 620 kilobytes) and runs at 60 hertz for a resolution of 1,920 x 1,080 pixels on a single consumer-grade graphics processing unit. Leveraging low-power on-device artificial intelligence acceleration chips, our CNN also runs interactively on mobile (iPhone 11 Pro at 1.1 hertz) and edge (Google Edge TPU at 2.0 hertz) devices, promising real-time performance in future-generation virtual and augmented-reality mobile headsets. We enable this pipeline by introducing a large-scale CGH dataset (MIT-CGH-4K) with 4,000 pairs of RGB-depth images and corresponding 3D holograms. Our CNN is trained with differentiable wave-based loss functions and physically approximates Fresnel diffraction. With an anti-aliasing phase-only encoding method, we experimentally demonstrate speckle-free, natural-looking, high-resolution 3D holograms. Our learning-based approach and the Fresnel hologram dataset will help to unlock the full potential of holography and enable applications in metasurface design, optical and acoustic tweezer-based microscopic manipulation, holographic microscopy and single-exposure volumetric 3D printing.
阅读原文 https://www.nature.com/articles/s41586-020-03152-0
 
文献三 三维卷积神经网络在头颈部血管造影血管快速分割与重建中的应用
Rapid vessel segmentation and reconstruction of head and neck angiograms using 3D convolutional neural network
Fu, Fan, etc.
NATURE COMMUNICATIONS, 2020, 11(1)
The computed tomography angiography (CTA) postprocessing manually recognized by technologists is extremely labor intensive and error prone. We propose an artificial intelligence reconstruction system supported by an optimized physiological anatomical-based 3D convolutional neural network that can automatically achieve CTA reconstruction in healthcare services. This system is trained and tested with 18,766 head and neck CTA scans from 5 tertiary hospitals in China collected between June 2017 and November 2018. The overall reconstruction accuracy of the independent testing dataset is 0.931. It is clinically applicable due to its consistency with manually processed images, which achieves a qualification rate of 92.1%. This system reduces the time consumed from 14.22 ± 3.64 min to 4.94 ± 0.36 min, the number of clicks from 115.87 ± 25.9 to 4 and the labor force from 3 to 1 technologist after five months application. Thus, the system facilitates clinical workflows and provides an opportunity for clinical technologists to improve humanistic patient care.
阅读原文 https://www.nature.com/articles/s41467-020-18606-2

文献四 基于大规模动作识别数据集，利用深度学习进行3D行为识别
NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding
Liu, Jun, etc.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2020, 42(10): 2684-2701
Research on depth-based human activity analysis achieved outstanding performance and demonstrated the effectiveness of 3D representation for action recognition. The existing depth-based and RGB+D-based action recognition benchmarks have a number of limitations, including the lack of large-scale training samples, realistic number of distinct class categories, diversity in camera views, varied environmental conditions, and variety of human subjects. In this work, we introduce a large-scale dataset for RGB+D human action recognition, which is collected from 106 distinct subjects and contains more than 114 thousand video samples and 8 million frames. This dataset contains 120 different action classes including daily, mutual, and health-related activities. We evaluate the performance of a series of existing 3D activity analysis methods on this dataset, and show the advantage of applying deep learning methods for 3D-based human action recognition. Furthermore, we investigate a novel one-shot 3D activity recognition problem on our dataset, and a simple yet effective Action-Part Semantic Relevance-aware (APSR) framework is proposed for this task, which yields promising results for recognition of the novel action classes. We believe the introduction of this large-scale dataset will enable the community to apply, adapt, and develop various data-hungry learning techniques for depth-based and RGB+D-based human activity understanding.
阅读原文 https://ieeexplore.ieee.org/document/8713892

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