We propose a conceptually simple framework for fast COVID-19 screening in 3D chest CT images. The framework can efficiently predict whether or not a CT scan contains pneumonia while simultaneously identifying pneumonia types between COVID-19 and Interstitial Lung Disease (ILD) caused by other viruses. In the proposed method, two 3D-ResNets are coupled together into a single model for the two above-mentioned tasks via a novel prior-attention strategy. We extend residual learning with the proposed prior-attention mechanism and design a new so-called priorattention residual learning (PARL) block. The model can be easily built by stacking the PARL blocks and trained endto-end using multi-task losses. More specifically, one 3D-ResNet branch is trained as a binary classifier using lung images with and without pneumonia so that it can highlight the lesion areas within the lungs. Simultaneously, inside the PARL blocks, prior-attention maps are generated from this branch and used to guide another branch to learn more discriminative representations for the pneumoniatype classification. Experimental results demonstrate that the proposed framework can significantly improve the performance of COVID-19 screening. Compared to other meth-Manuscript

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Telemedicine and the medical "big data" era in ophthalmology highlight the use of non-mydriatic ocular fundus photography, which has given rise to indispensable applications of portable fundus cameras. However, in the case of portable fundus photography, non-mydriatic image quality is more vulnerable to distortions, such as uneven illumination, color distortion, blur, and low contrast. Such distortions are called generic quality distortions. This paper proposes an algorithm capable of selecting images of fair generic quality that would be especially useful to assist inexperienced individuals in collecting meaningful and interpretable data with consistency. The algorithm is based on three characteristics of the human visual system--multi-channel sensation, just noticeable blur, and the contrast sensitivity function to detect illumination and color distortion, blur, and low contrast distortion, respectively. A total of 536 retinal images, 280 from proprietary databases and 256 from public databases, were graded independently by one senior and two junior ophthalmologists, such that three partial measures of quality and generic overall quality were classified into two categories. Binary classification was implemented by the support vector machine and the decision tree, and receiver operating characteristic (ROC) curves were obtained and plotted to analyze the performance of the proposed algorithm. The experimental results revealed that the generic overall quality classification achieved a sensitivity of 87.45% at a specificity of 91.66%, with an area under the ROC curve of 0.9452, indicating the value of applying the algorithm, which is based on the human vision system, to assess the image quality of non-mydriatic photography, especially for low-cost ophthalmological telemedicine applications.

Flexible, material‐based, artificial muscles enable compliant and safe technologies for human–machine interaction devices and adaptive soft robots, yet there remain long‐term challenges in the development of artificial muscles capable of mimicking flexible, controllable, and multifunctional human activity. Inspired by human limb's activity strategy, combining muscles' adjustable stiffness and joints' origami folding, controllable stiffness origami “skeletons,” which are created by laminar jamming and origami folding of multiple layers of flexible sandpaper, are embedded into a common monofunctional vacuumed‐powered cube‐shaped (CUBE) artificial muscle, thereby enabling the monofunctional CUBE artificial muscle to achieve lightweight and multifunctionality as well as controllable force/motion output without sacrificing its volume and shape. Successful demonstrations of arms self‐assembly and cooperatively gripping different objects and a “caterpillar” robot climbing different pipes illustrate high operational redundancy and high‐force output through “building blocks” assembly of multifunctional CUBE artificial muscles. Controllable stiffness origami “skeletons” offer a facile and low‐cost strategy to fabricate lightweight and multifunctional artificial muscles for numerous potential applications such as wearable assistant devices, miniature surgical instruments, and soft robots.