Computer vision and machine learning are playing an increasingly important role in computer-assisted diagnosis; however, the application of deep learning to medical imaging has challenges in data availability and data imbalance, and it is especially important that models for medical imaging are built to be trustworthy. Therefore, we propose TRUDLMIA, a trustworthy deep learning framework for medical image analysis, which adopts a modular design, leverages self-supervised pre-training, and utilizes a novel surrogate loss function. Experimental evaluations indicate that models generated from the framework are both trustworthy and high-performing. It is anticipated that the framework will support researchers and clinicians in advancing the use of deep learning for dealing with public health crises including COVID-19.

近年来，超级人性药物的研究与发展取得了显着发展，各种军事和商业应用程序越来越多。几个国家的公共和私人组织一直在投资超人员，旨在超越其竞争对手并确保/提高战略优势和威慑。对于这些组织而言，能够及时可靠地识别新兴技术至关重要。信息技术的最新进展使得分析大量数据，提取隐藏的模式并为决策者提供新的见解。在这项研究中，我们专注于2000 - 2020年期间有关高人物的科学出版物，并采用自然语言处理和机器学习来通过识别12个主要潜在研究主题并分析其时间演变来表征研究格局。我们的出版物相似性分析揭示了在研究二十年中表明周期的模式。该研究对研究领域进行了全面的分析，以及研究主题是算法提取的事实，可以从练习中删除主观性，并可以在主题和时间间隔之间进行一致的比较。

建立具有可信赖性的AI模型非常重要，尤其是在医疗保健等受监管的地区。在解决Covid-19时，以前的工作将卷积神经网络用作骨干建筑，该骨干建筑物易于过度宣告和过度自信做出决策，使它们不那么值得信赖 - 在医学成像背景下的关键缺陷。在这项研究中，我们提出了一种使用视觉变压器的功能学习方法，该方法使用基于注意力的机制，并检查变形金刚作为医学成像的新骨干结构的表示能力。通过对COVID-19胸部X光片进行分类的任务，我们研究了概括能力是否仅从视觉变形金刚的建筑进步中受益。通过使用“信任评分”计算和视觉解释性技术，对模型的可信度进行了定量和定性评估。我们得出的结论是，基于注意力的特征学习方法在建立可信赖的医疗保健深度学习模型方面有希望。

有效的代表学习是提高医学图像分析模型性能的关键。在培训深度学习模型中，常常必须在性能和信任之间进行妥协，这两者都对于医学应用至关重要。此外，用跨熵损失优化的模型往往遭受少数阶级的多数阶级和过于谨慎的无责任的过度交流。在这项工作中，我们将新的代理损失与自我监督学习进行了全新的替代损失，用于使用射线照相图像的Covid-19患者的计算机辅助筛查。此外，我们采用了新的量化分数来衡量模型的可信度。对特征学习方法和损失功能的性能和信任进行了消融研究。比较表明，利用新的替代损失对自我监督模型可以生产出高性能和值得信赖的标签有效的网络。

自动抄表技术尚未普遍。燃气，电或水积米读数主要由运营商或房主手动完成。在一些国家，操作员将拍照作为阅读证据，以通过与另一个运营商的离线检查和/或在发生冲突或投诉的情况下作为证据来确认阅读。整个过程是耗时，昂贵的，容易出错。自动化可以优化和促进这种劳动密集型和人类错误的过程。随着近期人工智能和计算机视野领域的进步，自动抄表系统比以往任何时候都变得越来越可行。最近在人工智能领域的近期进步，并受研究界的开源开放访问举措的启发，我们介绍了一个名为NRC-Gamma数据集的现实寿命燃气表图像的新型大型基准数据集。在2020年1月20日，在00:05 AM和11:59 PM之间，从Itron 400A隔膜煤气表收集数据。我们使用系统的方法来标记图像，验证标签，并确保注释的质量。数据集包含整个煤气表的28,883个图像以及左侧和右拨号显示器的57,766次裁剪图像。我们希望NRC-Gamma DataSet有助于研究界设计和实施准确，创新，智能，可重复的自动燃气表阅读解决方案。

In addition to its public health crisis, COVID-19 pandemic has led to the shutdown and closure of workplaces with an estimated total cost of more than $16 trillion. Given the long hours an average person spends in buildings and indoor environments, this research article proposes data-driven control strategies to design optimal indoor airflow to minimize the exposure of occupants to viral pathogens in built environments. A general control framework is put forward for designing an optimal velocity field and proximal policy optimization, a reinforcement learning algorithm is employed to solve the control problem in a data-driven fashion. The same framework is used for optimal placement of disinfectants to neutralize the viral pathogens as an alternative to the airflow design when the latter is practically infeasible or hard to implement. We show, via simulation experiments, that the control agent learns the optimal policy in both scenarios within a reasonable time. The proposed data-driven control framework in this study will have significant societal and economic benefits by setting the foundation for an improved methodology in designing case-specific infection control guidelines that can be realized by affordable ventilation devices and disinfectants.

Existing statistical methods can be used to estimate a policy, or a mapping from covariates to decisions, which can then instruct decision makers. There is great interest in using such data-driven policies in healthcare. In healthcare, however, it is often important to explain to the healthcare provider, and to the patient, how a new policy differs from the current standard of care. This end is facilitated if one can pinpoint the aspects (i.e., parameters) of the policy that change most when moving from the standard of care to the new, suggested policy. To this end, we adapt ideas from Trust Region Policy Optimization. In our work, however, unlike in Trust Region Policy Optimization, the difference between the suggested policy and standard of care is required to be sparse, aiding with interpretability. In particular, we trade off between maximizing expected reward and minimizing the $L_1$ norm divergence between the parameters of the two policies. This yields "relative sparsity," where, as a function of a tuning parameter, $\lambda$, we can approximately control the number of parameters in our suggested policy that differ from their counterparts in the standard of care. We develop our methodology for the observational data setting. We propose a problem-specific criterion for selecting $\lambda$, perform simulations, and illustrate our method with a real, observational healthcare dataset, deriving a policy that is easy to explain in the context of the current standard of care. Our work promotes the adoption of data-driven decision aids, which have great potential to improve health outcomes.

Considering the spectral properties of images, we propose a new self-attention mechanism with highly reduced computational complexity, up to a linear rate. To better preserve edges while promoting similarity within objects, we propose individualized processes over different frequency bands. In particular, we study a case where the process is merely over low-frequency components. By ablation study, we show that low frequency self-attention can achieve very close or better performance relative to full frequency even without retraining the network. Accordingly, we design and embed novel plug-and-play modules to the head of a CNN network that we refer to as FsaNet. The frequency self-attention 1) takes low frequency coefficients as input, 2) can be mathematically equivalent to spatial domain self-attention with linear structures, 3) simplifies token mapping ($1\times1$ convolution) stage and token mixing stage simultaneously. We show that the frequency self-attention requires $87.29\% \sim 90.04\%$ less memory, $96.13\% \sim 98.07\%$ less FLOPs, and $97.56\% \sim 98.18\%$ in run time than the regular self-attention. Compared to other ResNet101-based self-attention networks, FsaNet achieves a new state-of-the-art result ($83.0\%$ mIoU) on Cityscape test dataset and competitive results on ADE20k and VOCaug.

由于临床实践所需的放射学报告和研究是在自由文本叙述中编写和存储的，因此很难提取相对信息进行进一步分析。在这种情况下，自然语言处理（NLP）技术可以促进自动信息提取和自由文本格式转换为结构化数据。近年来，基于深度学习（DL）的模型已适用于NLP实验，并具有令人鼓舞的结果。尽管基于人工神经网络（ANN）和卷积神经网络（CNN）的DL模型具有显着潜力，但这些模型仍面临临床实践中实施的一些局限性。变形金刚是另一种新的DL体系结构，已越来越多地用于改善流程。因此，在这项研究中，我们提出了一种基于变压器的细粒命名实体识别（NER）架构，以进行临床信息提取。我们以自由文本格式收集了88次腹部超声检查报告，并根据我们开发的信息架构进行了注释。文本到文本传输变压器模型（T5）和covive是T5模型的预训练域特异性适应性，用于微调来提取实体和关系，并将输入转换为结构化的格式。我们在这项研究中基于变压器的模型优于先前应用的方法，例如基于Rouge-1，Rouge-2，Rouge-L和BLEU分别为0.816、0.668、0.528和0.743的ANN和CNN模型，同时提供了一个分数可解释的结构化报告。

作为其核心计算，一种自我发挥的机制可以在整个输入序列上分配成对相关性。尽管表现良好，但计算成对相关性的成本高昂。尽管最近的工作表明了注意力分数低的元素的运行时间修剪的好处，但自我发挥机制的二次复杂性及其芯片内存能力的需求被忽略了。这项工作通过构建一个称为Sprint的加速器来解决这些约束，该加速器利用RERAM横杆阵列的固有并行性以近似方式计算注意力分数。我们的设计使用RERAM内的轻质模拟阈值电路来降低注意力评分，从而使Sprint只能获取一小部分相关数据到芯片内存。为了减轻模型准确性的潜在负面影响，Sprint重新计算数字中少数获取数据的注意力评分。相关注意分数的组合内修剪和片上重新计算可以将Sprint转化为仅线性的二次复杂性。此外，我们即使修剪后，我们也可以识别并利用相邻的注意操作之间的动态空间位置，从而消除了昂贵但冗余的数据获取。我们在各种最新的变压器模型上评估了我们提出的技术。平均而言，当使用总16KB芯片内存时，Sprint会产生7.5倍的速度和19.6倍的能量，而实际上与基线模型的等值级相当（平均为0.36％的降级）。