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3D EAGAN: 3D edge-aware attention generative adversarial network for prostate segmentation in transrectal ultrasound images

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arXiv 2023年

作者： Liu, Mengqing Shao, Xiao Jiang, Liping Wu, Kaizhi School of Information Engineering Nanchang Hangkong University Jiangxi Nanchang China School of Computer Science Nanjing University of Information Science and Technology Jiangsu Nanjing China The First Affiliated Hospital of Nanchang University Nanchang University Jiangxi Nanchang China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Hangkong University Nanchang China

Background: Segment prostates from transrectal ultrasound (TRUS) images plays an essential role in the diagnosis and treatment of prostate cancer. However, traditional segmentation methods are time-consuming and laborious. To address this issue, there is an urgent need to develop computer algorithms that can automatically segment prostates from TRUS images, which makes it become the direction and form of future development. Purpose: Automatic prostate segmentation in TRUS images has always been a challenging problem, since prostates in TRUS images have ambiguous boundaries and inhomogeneous intensity distribution. Although many prostate segmentation methods have been proposed, they still need to be improved due to the lack of sensibility to edge information. Consequently, the objective of this study is to devise a highly effective prostate segmentation method that overcomes these limitations and achieves accurate segmentation of prostates in TRUS images. Methods: A 3D edge-aware attention generative adversarial network (3D EAGAN)-based prostate segmentation method is proposed in this paper, which consists of an edge-aware segmentation network (EASNet) that performs the prostate segmentation and a discriminator network that distinguishes predicted prostates from real prostates. The proposed EASNet is composed of an encoder-decoder-based U-Net backbone network, a detail compensation module, four 3D spatial and channel attention modules, an edge enhance module, and a global feature extractor. The detail compensation module is proposed to compensate for the loss of detailed information caused by the down-sampling process of the encoder. The features of the detail compensation module are selectively enhanced by the 3D spatial and channel attention module. Furthermore, an edge enhance module is proposed to guide shallow layers in the EASNet to focus on contour and edge information in prostates. Finally, features from shallow layers and hierarchical features from the decod

关键词： Generative adversarial networks

Understanding the Robustness of 3D Object Detection with Bird'View Representations in Autonomous Driving

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Understanding the Robustness of 3D Object Detection with Bir...

Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Zijian Zhu Yichi Zhang Hai Chen Yinpeng Dong Shu Zhao Wenbo Ding Jiachen Zhong Shibao Zheng Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Dept. of Comp. Sci. and Tech. THBI Lab Institute for AI Tsinghua University BNRist Center Zhongguancun Laboratory Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Information Materials and Intelligent Sensing Laboratory of Anhui Province School of Computer Science and Technology Anhui University SAIC Motor AI Lab

3D object detection is an essential perception task in autonomous driving to understand the environments. The Bird's-Eye-View (BEV) representations have significantly improved the performance of 3D detectors with camera inputs on popular benchmarks. However, there still lacks a systematic understanding of the robustness of these vision-dependent BEV models, which is closely related to the safety of autonomous driving systems. In this paper, we evaluate the natural and adversarial robustness of various representative models under extensive settings, to fully understand their behaviors influenced by explicit BEV features compared with those without BEV. In addition to the classic settings, we propose a 3D consistent patch attack by applying adversarial patches in the 3D space to guarantee the spatiotemporal consistency, which is more realistic for the scenario of autonomous driving. With substantial experiments, we draw several findings: 1) BEV models tend to be more stable than previous methods under different natural conditions and common corruptions due to the expressive spatial representations; 2) BEV models are more vulnerable to adversarial noises, mainly caused by the redundant BEV features; 3) Camera-LiDARfusion models have superior performance under different settings with multi-modal inputs, but BEV fusion model is still vulnerable to adversarial noises of both point cloud and image. These findings alert the safety issue in the applications of BEV detectors and could facilitate the development of more robust models.

关键词：

Understanding the Robustness of 3D Object Detection with Bird’s-Eye-View Representations in Autonomous Driving

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arXiv 2023年

作者： Zhu, Zijian Zhang, Yichi Chen, Hai Dong, Yinpeng Zhao, Shu Ding, Wenbo Zhong, Jiachen Zheng, Shibao Institute of Image Communication and Network Engineering Shanghai Jiao Tong University China Dept. of Comp. Sci. and Tech. Institute for AI THBI Lab BNRist Center Tsinghua University China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education School of Computer Science and Technology Anhui University Information Materials and Intelligent Sensing Laboratory of Anhui Province China SAIC Motor AI Lab China Zhongguancun Laboratory China

3D object detection is an essential perception task in autonomous driving to understand the environments. The Bird’s-Eye-View (BEV) representations have significantly improved the performance of 3D detectors with camera inputs on popular benchmarks. However, there still lacks a systematic understanding of the robustness of these vision-dependent BEV models, which is closely related to the safety of autonomous driving systems. In this paper, we evaluate the natural and adversarial robustness of various representative models under extensive settings, to fully understand their behaviors influenced by explicit BEV features compared with those without BEV. In addition to the classic settings, we propose a 3D consistent patch attack by applying adversarial patches in the 3D space to guarantee the spatiotemporal consistency, which is more realistic for the scenario of autonomous driving. With substantial experiments, we draw several findings: 1) BEV models tend to be more stable than previous methods under different natural conditions and common corruptions due to the expressive spatial representations;2) BEV models are more vulnerable to adversarial noises, mainly caused by the redundant BEV features;3) Camera-LiDAR fusion models have superior performance under different settings with multi-modal inputs, but BEV fusion model is still vulnerable to adversarial noises of both point cloud and image. These findings alert the safety issue in the applications of BEV detectors and could facilitate the development of more robust models. Code available at https: //***/zzj403/BEV_Robust. Copyright © 2023, The Authors. All rights reserved.

关键词： Birds

Smoothing Energy Transfer Enabling Efficient Large-Area Qusai-2d Perovskite Light-Emitting Diodes

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SSRN

SSRN 2022年

作者： Lei, Cai Zang, Jiaqing Jiangyu, Chen Jungui, Zhou Yatao, Zou Guilin, Bai Zhewei, Chen Song, Tao Chuan-Kui, Wang Sun, Baoquan Shandong Province Key Laboratory of Medical Physics and Image Processing Technology School of Physics and Electronics Shandong Normal University Shandong Jinan250014 China Soochow University Jiangsu Suzhou215123 China Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices Soochow University Jiangsu Suzhou215123 China MUST-SUDA Joint Research Center for Advanced Functional Materials Macau University of Science and Technology Taipa999078 China

Electroluminescence (EL) efficiency of perovskite light-emitting diodes (PeLEDs) based on a few square millimeters has improved significantly in recent years. Nevertheless, the EL efficiency of PeLEDs would be plunged once the active area was enlarged from millimeter to even sub-centimeter due to the unsmooth energy transfer process among the edge region with numerous nonradiative recombination centers. Herein, we developed an intriguing strategy to realize high-quality quasi-two-dimensional (quasi-2D) perovskite thin film via tuning perovskite precursor rheological properties as well as modulating the substrate surface tension. The perovskite crystallization process was retarded by incorporating a strong chelating ligand into its precursor. Hydroxylamine-O-sulfonic acid (ASC), containing a sulfonic acid group and an amino group acted as a strong chelating agent with lead ions (Pb 2+ ), which exhibited a great synergistic potential in defect passivation and crystallization modulation. An atop ultrathin lithium fluoride interlayer was pre-deposited to boost the substrate surface tension resulting in full coverage on a large-area substrate. ASC-anchored quasi-2D intermediate phases delayed the crystallization due to the strong chemical interaction between ASC and perovskite and then suppressed small-nanoplatelet phase formation in quasi-2D perovskite. ASC additives can also suppress nonradiative recombination, which is ascribed to the high binding energy between high electro positivity of the amino group with negatively charged defects as well as the elimination of uncoordinated Pb 2+ by the electron-rich sulfonic acid group. As a result, a large-area (25 cm 2 ) quasi-2D PeLED achieves an external quantum efficiency of 20.7% with uniform emitting characteristics, a record value among analogous same-size PeLEDs. Our work may pave the way to realizing high-performance large-area perovskite optoelectronic devices. © 2022, The Authors. All rights reserved.

关键词： Light emitting diodes

Enhancing emission performance of red TADF emitters via the introduction of electronically inert pendant

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Organic Electronics 2024年 126卷

作者： Tang, Yukun He, Jian-Li Zhang, Kai Zhao, Yue Lin, Yu-Ching Chen, Chia-Hsun Chiu, Tien-Lung Lee, Jiun-Haw Wang, Chuan-Kui Fan, Jian Wu, Chung-Chih Department of Electrical Engineering Graduate Institute of Electronics Engineering and Graduate Institute of Photonics and Optoelectronics National Taiwan University Taipei 10617 Taiwan Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices Soochow University Jiangsu Suzhou215123 China Shandong Province Key Laboratory of Medical Physics and Image Processing Technology School of Physics and Electronics Shandong Normal University Jinan250014 China Coordination Chemistry Institute State Key Laboratory of Coordination Chemistry School of Chemistry and Chemical Engineering Nanjing National Laboratory of Microstructures Nanjing University Nanjing210023 China Department of Chemistry National Taiwan University Taipei10617 Taiwan Department of Electrical Engineering Yuan Ze University Taoyuan32003 Taiwan Graduate Institute of Photonics and Optoelectronics and Department of Electrical Engineering National Taiwan University Taipei10617 Taiwan State Key Laboratory of Structural Chemistry Fujian Institute of Research on Structure of Matter Chinese Academy of Sciences Fujian Fuzhou35002 China

In addition to emission efficiency and colors, emission characteristics such as preferentially horizontal emitting dipole orientation and resistance against concentration quenching are also highly desired for high-performance thermally activated delayed fluorescence (TADF) emitters. In this work, we report a simple and yet effective strategy for enhancing such emission performance of the pure red-to-near infrared (NIR) TADF emitter by introducing bulky aryl derivatives (m-xylene) to the efficient emission core. Incorporating m-xylene groups as the electronically inert pendants to the acceptor unit of the TADF emitter TPA–CN–N4-CH3 successfully retains favorable emission properties (e.g., pure red color chromaticity, nearly unitary photoluminescence quantum yield etc.) of the parent compound TPA–CN–N4 and yet also renders significantly higher horizontal emitting dipole ratio Θ// of ∼85 % with the more extended molecular configuration and reduced concentration quenching at very high concentrations in thin films. As a result, OLEDs adopting TPA–CN–N4-CH3 can demonstrate rather high EQEs of 29.8–32.1 % for pure red emission (625–650 nm) at low doping concentrations and decent EQEs of 18.9–27.5 % in the deep red to NIR range (650–700 nm) at higher doping concentrations. The results of this work shall provide a useful reference for development of efficient TADF emitters. © 2024 Elsevier B.V.

关键词： Fluorescence

Lightweight Real-time Semantic Segmentation Network with Efficient Transformer and CNN

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arXiv 2023年

作者： Xu, Guoan Li, Juncheng Gao, Guangwei Lu, Huimin Yang, Jian Yue, Dong The Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing210023 China The Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou215006 China The School of Communication and Information Engineering Shanghai University Shanghai200444 China Jiangsu Key Laboratory of Image and Video Understanding for Social Safety Nanjing University of Science and Technology Nanjing210049 China The Kyushu Institute of Technology Kitakyushu804-8550 Japan The School of Computer Science and Technology Nanjing University of Science and Technology Nanjing210049 China

In the past decade, convolutional neural networks (CNNs) have shown prominence for semantic segmentation. Although CNN models have very impressive performance, the ability to capture global representation is still insufficient, which results in suboptimal results. Recently, Transformer achieved huge success in NLP tasks, demonstrating its advantages in modeling long-range dependency. Recently, Transformer has also attracted tremendous attention from computer vision researchers who reformulate the image processing tasks as a sequence-to-sequence prediction but resulted in deteriorating local feature details. In this work, we propose a lightweight real-time semantic segmentation network called LETNet. LETNet combines a U-shaped CNN with Transformer effectively in a capsule embedding style to compensate for respective deficiencies. Meanwhile, the elaborately designed Lightweight Dilated Bottleneck (LDB) module and Feature Enhancement (FE) module cultivate a positive impact on training from scratch simultaneously. Extensive experiments performed on challenging datasets demonstrate that LETNet achieves superior performances in accuracy and efficiency balance. Specifically, It only contains 0.95M parameters and 13.6G FLOPs but yields 72.8% mIoU at 120 FPS on the Cityscapes test set and 70.5% mIoU at 250 FPS on the CamVid test dataset using a single RTX 3090 GPU. Source code will be available at https://***/IVIPLab/LETNet. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantic Segmentation

EWT: Efficient Wavelet-Transformer for Single image Denoising

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arXiv 2023年

作者： Li, Juncheng Cheng, Bodong Chen, Ying Gao, Guangwei Zeng, Tieyong The School of Communication & Information Engineering Shanghai University Shanghai China Jiangsu Key Laboratory of Image and Video Understanding for Social Safety Nanjing University of Science and Technology Nanjing China The School of Computer Science and Technology Xidian University Xian China The Department of Cyberspace Security Beijing Electronic Science & Technology Institute Beijing China The Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing China The Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou China The Department of Mathematics The Chinese University of Hong Kong New Territories Hong Kong

Transformer-based image denoising methods have achieved encouraging results in the past year. However, it must uses linear operations to model long-range dependencies, which greatly increases model inference time and consumes GPU storage space. Compared with convolutional neural network-based methods, current Transformer-based image denoising methods cannot achieve a balance between performance improvement and resource consumption. In this paper, we propose an Efficient Wavelet Transformer (EWT) for image denoising. Specifically, we use Discrete Wavelet Transform (DWT) and Inverse Wavelet Transform (IWT) for downsampling and upsampling, respectively. This method can fully preserve the image features while reducing the image resolution, thereby greatly reducing the device resource consumption of the Transformer model. Furthermore, we propose a novel Dual-stream Feature Extraction Block (DFEB) to extract image features at different levels, which can further reduce model inference time and GPU memory usage. Experiments show that our method speeds up the original Transformer by more than 80%, reduces GPU memory usage by more than 60%, and achieves excellent denoising results. All code will be public. © 2023, CC BY.

关键词： image denoising

Millimeter-wave radar image analysis for the traffic sensing 2

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Millimeter-wave radar image analysis for the traffic sensing

2nd Spring International Conference on Defence Technology, ICDT 2020

作者： Wu, H.M. Qi, F. Wang, J.K. Wang, Y. School of Communication Science and Engineering Northeastern University Liaoning Province Shenyang110819 China Shenyang Institute of Automation Chinese Academy of Sciences China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences China Key Laboratory of Opto-Electronic Information Processing Chinese Academy of Sciences China Key Lab of Image Understanding and Computer Vision Liaoning Province Shenyang110016 China

Millimeter-wave(MMW) radar sensing is one of the most promising technologies to provide safe navigation for autonomous vehicles due to its expected high-resolution imaging capability However, driverless cars have higher request for different environment and light conditions. Therefore, millimetre-wave imaging is of paramount importance for complex load scenario. In this paper, we have built models of pavement pits and bulges and analysed their with differences ways of antennas. A comparison of the imaging performance of experimental systems operating at a MMW radar and a Lidar is presented with the analysis of features for initial image interpretation Experimental images of the complex road surface are made by a 94GHz frequency-modulated continuous-wave (FMCW) radar technique with 3mm wavelength. © Published under licence by IOP Publishing Ltd.

关键词： Optical radar

13.2% EQE near-infrared TADF OLED with emission peak at 761 nm

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Chemical Engineering Journal 2023年 452卷

作者： Tai, Jing-Wen Tang, Yukun Zhang, Kai Yang, Chen-Zong Pan, Ze-Hui Lin, Yu-Ching Shih, Yu-Wei Chen, Chia-Hsun Chiu, Tien-Lung Lee, Jiun-Haw Wang, Chuan-Kui Wu, Chung-Chih Fan, Jian Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices Soochow University Jiangsu Suzhou215123 China Department of Electrical Engineering Graduate Institute of Electronics Engineering and Graduate Institute of Photonics and Optoelectronics National Taiwan University Taipei10617 Taiwan Shandong Province Key Laboratory of Medical Physics and Image Processing Technology School of Physics and Electronics Shandong Normal University Jinan250014 China Department of Chemistry National Taiwan University Taipei10617 Taiwan Department of Electrical Engineering Yuan Ze University Taoyuan32003 Taiwan Graduate Institute of Photonics and Optoelectronics and Department of Electrical Engineering National Taiwan University Taipei10617 Taiwan State Key Laboratory of Structural Chemistry Fujian Institute of Research on Structure of Matter Chinese Academy of Sciences Fujian Fuzhou35002 China

Near infrared (NIR) emitters have attracted much attention due to their advanced application in biomedical sensing/imaging, and telecommunications. However, the development of efficient NIR thermally activated delayed fluorescence (TADF) emitters with the emission over 750 nm is still a challenge. Herein two Y-shaped NIR TADF molecules (Py-TPA and Py-CN-TPA) were introduced. The inclusion of pyridine unit in these two emitters not only extended the conjugated system but also increased the molecular aspect ratios, thereby leading to their high horizontal ratio of emitting dipole orientation (Θ//). In addition, the NIR emitter showed a low Huang-Rhys (HR) factor in thin film relative to that in solution, indicating a low non-radiative transition rate and a high photoluminescent quantum yield (PLQY). Consequently, TADF OLED based on Py-TPA demonstrated a record-high external quantum efficiency (EQE) of 22.2% at 726 nm, 16.3% at 747 nm, and 13.2% at 761 nm. © 2022 Elsevier B.V.

关键词： Aspect ratio