Recently, the coronavirus disease 2019 (COVID-19) has caused a pandemic disease in over 200 countries, influencing billions of humans. To control the infection, identifying and separating the infected people is the most crucial step. The main diagnostic tool is the Reverse Transcription Polymerase Chain Reaction (RT-PCR) test. Still, the sensitivity of the RT-PCR test is not high enough to effectively prevent the pandemic. The chest CT scan test provides a valuable complementary tool to the RT-PCR test, and it can identify the patients in the early-stage with high sensitivity. However, the chest CT scan test is usually time-consuming, requiring about 21.5 minutes per case. This paper develops a novel Joint Classification and Segmentation (JCS) system to perform real-time and explainable COVID-19 chest CT diagnosis. To train our JCS system, we construct a large scale COVID-19 Classification and Segmentation (COVID-CS) dataset, with 144,167 chest CT images of 400 COVID-19 patients and 350 uninfected cases. 3,855 chest CT images of 200 patients are annotated with fine-grained pixel-level labels of opacifications, which are increased attenuation of the lung parenchyma. We also have annotated lesion counts, opacification areas, and locations and thus benefit various diagnosis aspects. Extensive experiments demonstrate that the proposed JCS diagnosis system is very efficient for COVID-19 classification and segmentation. It obtains an average sensitivity of 95.0% and a specificity of 93.0% on the classification test set, and 78.5% Dice score on the segmentation test set of our COVID-CS dataset. The COVID-CS dataset and code are available at this https URL.

JCS: An Explainable COVID-19 Diagnosis System by Joint Classification and Segmentation

Recently, the coronavirus disease 2019 (COVID-19) has caused a pandemic disease in over 200 countries, influencing billions of humans. To control the infection, identifying and separating the infected people is the most crucial step. The main diagnostic tool is the Reverse Transcription Polymerase Chain Reaction (RT-PCR) test. Still, the sensitivity of the RT-PCR test is not high enough to effectively prevent the pandemic. The chest CT scan test provides a valuable complementary tool to the RT-PCR test, and it can identify the patients in the early-stage with high sensitivity. However, the chest CT scan test is usually time-consuming, requiring about 21.5 minutes per case. This paper develops a novel Joint Classification and Segmentation ( JCS ) system to perform real-time and explainable COVID- 19 chest CT diagnosis. To train our JCS system, we construct a large scale COVID- 19 Classification and Segmentation ( COVID-CS ) dataset, with 144,167 chest CT images of 400 COVID- 19 patients and 350 uninfected cases. 3,855 chest CT images of 200 patients are annotated with fine-grained pixel-level labels of opacifications, which are increased attenuation of the lung parenchyma. We also have annotated lesion counts, opacification areas, and locations and thus benefit various diagnosis aspects. Extensive experiments demonstrate that the proposed JCS diagnosis system is very efficient for COVID-19 classification and segmentation. It obtains an average sensitivity of 95.0% and a specificity of 93.0% on the classification test set, and 78.5% Dice score on the segmentation test set of our COVID-CS dataset. The COVID-CS dataset and code are available at https://github.com/yuhuan-wu/JCS .

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Recent progress on salient object detection (SOD) mostly benefits from the explosive development of Convolutional Neural Networks (CNNs). However, much of the improvement comes with the larger network size and heavier computation overhead, which, in our view, is not mobile-friendly and thus difficult to deploy in practice. To promote more practical SOD systems, we introduce a novel Stereoscopically Attentive Multi-scale (SAM) module, which adopts a stereoscopic attention mechanism to adaptively fuse the features of various scales. Embarking on this module, we propose an extremely lightweight network, namely SAMNet, for SOD. Extensive experiments on popular benchmarks demonstrate that the proposed SAMNet yields comparable accuracy with state-of-the-art methods while running at a GPU speed of 343fps and a CPU speed of 5fps for $336 \times 336$ inputs with only 1.33M parameters. Therefore, SAMNet paves a new path towards SOD. The source code is available on the project page https://mmcheng.net/SAMNet/ .

SAMNet: Stereoscopically Attentive Multi-Scale Network for Lightweight Salient Object Detection

Encyclopedia of Computational Neuroscience

The prevailing applications of Unmanned Aerial Vehicles (UAVs) in transportation systems promote the development of object detection methods to collect real-time traffic information through UAVs. However, due to the small size and high density of objects from the aerial perspective, most existing algorithms are difficult to accurately process and extract informative features from the traffic images collected by UAVs. To address the challenges, this paper proposes a new real-time small object detection (RSOD) algorithm based on YOLOv3, which improves the small object detection accuracy by (i) using feature maps of a shallower layer containing more fine-grained information for location prediction; (ii) fusing local and global features of shallow and deep feature maps in Feature Pyramid Network(FPN) to enhance the ability to extract more representative features; (iii)assigning weights to output features of FPN and fusing them adaptively; and(iv) improving the excitation layer in Squeeze-and-Excitation attention mechanism to adjust the feature responses of each channel more precisely. Experimental results show that, when the input size is 608 × 608 × 3, the precision of the proposed RSOD algorithm measured by mAP@0.5 is 43.3% and 52.7% on the Visdrone-DET2018 and UAVDT datasets, which is 3.4% and 5.1% higher than those of YOLOv3, respectively.

RSOD: Real-time small object detection algorithm in UAV-based traffic monitoring

Recently, salient object detection (SOD) has witnessed vast progress with the rapid development of convolutional neural networks (CNNs). However, the improvement of SOD accuracy comes with the increase in network depth and width, resulting in large network size and heavy computational overhead. This prevents state-of-the-art SOD methods from being deployed into practical platforms, especially mobile devices. To promote the deployment of real-world SOD applications, we aim at developing a lightweight SOD model in this article. Our observation comes from that the primate visual system processes visual signals hierarchically with different receptive fields and eccentricities in different visual cortex areas. Inspired by this, we propose a hierarchical visual perception (HVP) module to imitate the primate visual cortex for hierarchical perception learning. With the HVP module incorporated, we design a lightweight SOD network, namely, HVPNet. Extensive experiments on popular benchmarks demonstrate that HVPNet achieves highly competitive accuracy compared with state-of-the-art SOD methods while running at 4.3 frames/s CPU speed and 333.2 frames/s GPU speed with only 1.23M parameters.

Lightweight Salient Object Detection via Hierarchical Visual Perception Learning

Layered Ti-based oxide hierarchical nanocomposites derived from flash oxidation of MXene with enhanced photocatalytic and microwave transparent performance

Weiwei Wang

Papers

Lightweight Salient Object Detection via Hierarchical Visual Perception Learning

Layered Ti-based oxide hierarchical nanocomposites derived from flash oxidation of MXene with enhanced photocatalytic and microwave transparent performance

MircroRNA-145 Attenuates Cardiac Fibrosis Via Regulating Mitogen-Activated Protein Kinase Kinase Kinase 3

Shear Mechanism of Steel-Asphalt Interface Using Three-Dimensional Discrete-Continuous Coupling Method on the Mesoscopic Scale