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arXiv 2022年

作者： Cong, Runmin Song, Weiyu Lei, Jianjun Yue, Guanghui Zhao, Yao Kwong, Sam The Institute of Information Science Beijing Jiaotong University Beijing100044 China The Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China The Department of Computer Science City University of Hong Kong Hong Kong The School of Electrical and Information Engineering Tianjin University Tianjin300072 China The National Regional Key Technology Engineering Laboratory for Medical Ultrasound Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging School of Biomedical Engineering Health Science Center Shenzhen University Shenzhen518060 China The City University of Hong Kong Shenzhen Research Institute Shenzhen51800 China

For the video salient object detection (VSOD) task, how to excavate the information from the appearance modality and the motion modality has always been a topic of great concern. The two-stream structure, including an RGB appearance stream and an optical flow motion stream, has been widely used as a typical pipeline for VSOD tasks, but the existing methods usually only use motion features to unidirectionally guide appearance features or adaptively but blindly fuse two modality features. However, these methods underperform in diverse scenarios due to the uncomprehensive and unspecific learning schemes. In this paper, following a more secure modeling philosophy, we deeply investigate the importance of appearance modality and motion modality in a more comprehensive way and propose a VSOD network with up and down parallel symmetry, named PSNet. Two parallel branches with different dominant modalities are set to achieve complete video saliency decoding with the cooperation of the Gather Diffusion Reinforcement (GDR) module and Cross-modality Refinement and Complement (CRC) module. Finally, we use the Importance Perception Fusion (IPF) module to fuse the features from two parallel branches according to their different importance in different scenarios. Experiments on four dataset benchmarks demonstrate that our method achieves desirable and competitive performance. The code and results can be found from the link of https://***/proj_***. © 2022, CC BY-NC-SA.

关键词： Benchmarking

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Quantifying U-Net Uncertainty in Multi-Parametric MRI-based Glioma Segmentation by Spherical Image Projection

arXiv

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arXiv 2022年

作者： Yang, Zhenyu Lafata, Kyle Vaios, Eugene Hu, Zongsheng Mullikin, Trey Yin, Fang-Fang Wang, Chunhao Department of Radiation Oncology Duke University DurhamNC United States Medical Physics Graduate Program Duke Kunshan University Jiangsu Kunshan China Medical Physics Graduate Program Duke University DurhamNC United States Department of Radiology Duke University DurhamNC United States Department of Electrical and Computer Engineering Duke University DurhamNC United States Department of Radiation Physics The University of Texas MD Anderson Cancer Center HoustonTX United States The University of Texas MD Anderson Graduate School of Biomedical Science HoustonTX United States

Background: Uncertainty quantification in deep learning is an important research topic. For medical image segmentation, the uncertainty measurements are usually reported as the likelihood that each pixel belongs to the predicted segmentation region. In potential clinical applications, the uncertainty result reflects the algorithm's robustness and supports the confidence and trust of the segmentation result when the ground-truth result is absent. For commonly studied deep learning models, novel methods for quantifying segmentation uncertainty are in demand. Purpose: To develop a U-Net segmentation uncertainty quantification method based on spherical image projection of multi-parametric MRI (MP-MRI) in glioma segmentation. Methods: The projection of planar MRI data onto a spherical surface is equivalent to a nonlinear image transformation that retains global anatomical information. By incorporating this image transformation process in our proposed spherical projection-based U-Net (SPU-Net) segmentation model design, multiple independent segmentation predictions can be obtained from a single MRI. The final segmentation is the average of all available results, and the variation can be visualized as a pixel-wise uncertainty map. An uncertainty score was introduced to evaluate and compare the performance of uncertainty measurements. The proposed SPU-Net model was implemented on the basis of 369 glioma patients with MP-MRI scans (T1, T1-Ce, T2, and FLAIR). Three SPU-Net models were trained to segment enhancing tumor (ET), tumor core (TC), and whole tumor (WT), respectively. The SPU-Net model was compared with (1) the classic U-Net model with test-time augmentation (TTA) and (2) linear scaling-based U-Net (LSU-Net) segmentation models in terms of both segmentation accuracy (Dice coefficient, sensitivity, specificity, and accuracy) and segmentation uncertainty (uncertainty map and uncertainty score). Results: The developed SPU-Net model successfully achieved low uncertainty

关键词： Tumors

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Quasi-Real Time Multi-Frequency 3D Shear Wave Absolute Vibro-Elastography (S-WAVE) System for Prostate

arXiv

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arXiv 2022年

作者： Aleef, Tajwar Abrar Lobo, Julio Baghani, Ali Eskandari, Hani Moradi, Hamid Rohling, Robert Goldenberg, S. Larry Morris, William James Mahdavi, S. Sara Salcudean, Septimiu E. The School of Biomedical Engineering University of British Columbia VancouverBC Canada The Department of Electrical and Computer Engineering University of British Columbia VancouverBC Canada Sonic Incytes Medical Corp. VancouverBC Canada Salesforce TorontoON Canada Ladybug Robotics VancouverBC Canada BC Cancer - Vancouver Centre BC Canada The Department of Urological Sciences University of British Columbia VancouverBC Canada Radiation Oncologist BC Cancer - Vancouver Centre BC Canada

This article describes a novel quasi-real time system for quantitative and volumetric measurement of tissue elasticity in the prostate. Tissue elasticity is computed by using a local frequency estimator to measure the three dimensional local wavelengths of a steady-state shear wave within the prostate gland. The shear wave is created using a mechanical voice coil shaker which transmits multi-frequency vibrations transperineally. Radio frequency data is streamed directly from a BK Medical 8848 trans-rectal ultrasound transducer to an external computer where tissue displacement due to the excitation is measured using a speckle tracking algorithm. Bandpass sampling is used that eliminates the need for an ultra fast frame rate to track the tissue motion and allows for accurate reconstruction at a sampling frequency that is below the Nyquist rate. A roll motor with computer control is used to rotate the sagittal array of the transducer and obtain the 3D data. Two CIRS phantoms were used to validate both the accuracy of the elasticity measurement as well as the functional feasibility of using the system for in vivo prostate imaging. The system has been used in two separate clinical studies as a method for cancer identification. The results, presented here, show numerical and visual correlations between our stiffness measurements and cancer likelihood as determined from pathology results. Initial published results using this system include an area under the receiver operating characteristic curve of 0.82±0.01 with regards to prostate cancer identification in the peripheral zone. © 2022, CC BY-NC-ND.

关键词： Diseases

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Current and future directions in network biology

arXiv

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arXiv 2023年

作者： Zitnik, Marinka Li, Michelle M. Wells, Aydin Glass, Kimberly Gysi, Deisy Morselli Krishnan, Arjun Murali, T.M. Radivojac, Predrag Roy, Sushmita Baudot, Anaïs Bozdag, Serdar Chen, Danny Z. Cowen, Lenore Devkota, Kapil Gitter, Anthony Gosline, Sara Gu, Pengfei Guzzi, Pietro H. Huang, Heng Jiang, Meng Kesimoglu, Ziynet Nesibe Koyuturk, Mehmet Ma, Jian Pico, Alexander R. Pržulj, Nataša Przytycka, Teresa M. Raphael, Benjamin J. Ritz, Anna Sharan, Roded Shen, Yang Singh, Mona Slonim, Donna K. Tong, Hanghang Yang, Xinan Holly Yoon, Byung-Jun Yu, Haiyuan Milenković, Tijana Department of Biomedical Informatics Harvard University United States Department of Computer Science and Engineering University of Notre Dame United States Lucy Family Institute for Data and Society University of Notre Dame United States Eck Institute for Global Health University of Notre Dame United States Channing Division of Network Medicine Brigham and Women’s Hospital Harvard Medical School United States Department of Statistics Federal University of Paraná Brazil Department of Physics Northeastern University United States Department of Biomedical Informatics University of Colorado United States Department of Computer Science Virginia Tech United States Khoury College of Computer Sciences Northeastern University United States Department of Biostatistics and Medical Informatics University of Wisconsin-Madison United States Wisconsin Institute for Discovery United States Aix Marseille Université INSERM MMG Marseille France Department of Computer Science and Engineering University of North Texas United States Department of Mathematics University of North Texas United States Department of Computer Science Tufts University United States Morgridge Institute for Research United States Pacific Northwest National Laboratory United States Department of Medical and Surgical Sciences University Magna Graecia of Catanzaro Italy Department of Computer Science University of Maryland College Park United States National Center of Biotechnology Information National Library of Medicine National Institutes of Health Department of Computer and Data Sciences Case Western Reserve University United States Computational Biology Department School of Computer Science Carnegie Mellon University United States Institute of Data Science and Biotechnology Gladstone Institutes Department of Computer Science University College London United Kingdom ICREA Catalan Institution for Research and Advanced Studies Spain Department of Computer Science Princeton University United States Departmen

Network biology is an interdisciplinary field bridging computational and biological sciences that has proved pivotal in advancing the understanding of cellular functions and diseases across biological systems and scales. Although the field has been around for two decades, it remains nascent. It has witnessed rapid evolution, accompanied by emerging challenges. These challenges stem from various factors, notably the growing complexity and volume of data together with the increased diversity of data types describing different tiers of biological organization. We discuss prevailing research directions in network biology and highlight areas of inference and comparison of biological networks, multimodal data integration and heterogeneous networks, higher-order network analysis, machine learning on networks, and network-based personalized medicine. Following the overview of recent breakthroughs across these five areas, we offer a perspective on the future directions of network biology. Additionally, we offer insights into scientific communities, educational initiatives, and the importance of fostering diversity within the field. This paper establishes a roadmap for an immediate and long-term vision for network biology. © 2023, CC BY.

关键词： Machine learning

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Plug-and-play stability for intracortical brain-computer interfaces: a one-year demonstration of seamless brain-to-text communication 23

Plug-and-play stability for intracortical brain-computer int...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Chaofei Fan Nick Hahn Foram Kamdar Donald Avansino Guy H. Wilson Leigh Hochberg Krishna V. Shenoy Jaimie M. Henderson Francis R. Willett Department of Computer Science Stanford University Department of Neurosurgery Stanford University Howard Hughes Medical Institute at Stanford University Department of Neuroscience Stanford University School of Engineering and Carney Institute for Brain Science Brown University and VA RR & D Center for Neurorestoration and Neurotechnology VA Providence Healthcare System and Center for Neurotechnology and Neurorecovery Department of Neurology Massachusetts General Hospital Harvard Medical School Bio-X Program Stanford University and Department of Neurobiology Stanford University and Department of Bioengineering Stanford University and Wu Tsai Neurosciences Institute Stanford University and Howard Hughes Medical Institute at Stanford University and Department of Electrical Engineering Stanford University Department of Neurosurgery Stanford University and Wu Tsai Neurosciences Institute Stanford University

Intracortical brain-computer interfaces (iBCIs) have shown promise for restoring rapid communication to people with neurological disorders such as amyotrophic lateral sclerosis (ALS). However, to maintain high performance over time, iBCIs typically need frequent recalibration to combat changes in the neural recordings that accrue over days. This requires iBCI users to stop using the iBCI and engage in supervised data collection, making the iBCI system hard to use. In this paper, we propose a method that enables self-recalibration of communication iBCIs without interrupting the user. Our method leverages large language models (LMs) to automatically correct errors in iBCI outputs. The self-recalibration process uses these corrected outputs ("pseudo-labels") to continually update the iBCI decoder online. Over a period of more than one year (403 days), we evaluated our Continual Online Recalibration with Pseudo-labels (CORP) framework with one clinical trial participant. CORP achieved a stable decoding accuracy of 93.84% in an online handwriting iBCI task, significantly outperforming other baseline methods. Notably, this is the longest-running iBCI stability demonstration involving a human participant. Our results provide the first evidence for long-term stabilization of a plug-and-play, high-performance communication iBCI, addressing a major barrier for the clinical translation of iBCIs.

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MT8 Cost-Consequences Analysis (CCA) of Echocardiography Digital Artificial Intelligence (AI) Monitoring Application Developed at Hosmartai (Horizon 2020 Funded)

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Value in Health 2024年第12期27卷 S488-S488页

作者： Chatzikou, M Latsou, D Apostolidis, G Charisis, V Hadjidimitriou, S Hadjileontiadis, L Pagkourelias, E Vassilikos, V Pharmecons Easy Access LtD York UK Signal Processing & Biomedical Technology Unit Department of Electrical & Computer Engineering Aristotle University of Thessaloniki Thessaloniki Greece Department of Biomedical Engineering Khalifa University Abu Dhabi Abu Dhabi United Arab Emirates 3rd Cardiology Department Hippokrateion Hospital Medical School Aristotle University of Thessaloniki Thessaloniki Greece

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Review of Image-Guided Percutaneous Coronary Interventions

Review of Image-Guided Percutaneous Coronary Interventions

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2019 Advances in Science and engineering Technology International Conferences, ASET 2019

作者： Alatoom, Aseel Farhat, Jumana Dhou, Salam Biomedical Engineering Graduate Program American University of Sharjah Sharjah United Arab Emirates Department of Computer Science and Engineering Biomedical Engineering Graduate Program Biosciences and Bioengineering Research Institute American University of Sharjah Sharjah United Arab Emirates

ISBN: (纸本)9781538682715

Image-guided surgery (IGS) involves using surgical instruments tracking system to guide the surgeon during invasive surgical procedures. The use of preoperative or intraoperative images is very essential in providing navigation for different medical applications. In this paper, a literature review of different imaging modalities that aid in the diagnosis and treatment of coronary artery disease (CAD) is proposed. The imaging modalities discussed include x-ray fluoroscopy, coronary computed tomography angiography (CCTA), fractional flow reserve (FFR), cardiac single-photon emission computed tomography (SPECT), cardiovascular magnetic resonance imaging (CMRI) and ultrasound. Additionally, pseudoaneurysms, which can result from percutaneous coronary interventions (PCI) procedure, are reviewed as well as the treatment procedure. Finally, a comparison between the different imaging modalities is carried out, based on the number of papers that have been published, in addition to the sensitivity and specificity for each modality according to PubMed and Web of Science databases. © 2019 IEEE.

关键词： Ultrasonics

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Model-centric volumetric point cloud attributes

arXiv

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arXiv 2021年

作者： de Queiroz, Ricardo L. Dorea, Camilo Freitas, Davi R. Krivokuća, Maja Sandri, Gustavo P. Computer Science Department Universidade de Brasilia Brasilia Brazil Post-Graduate Program at the Electrical Engineering Science Department Universidade de Brasilia Brasilia Brazil INRIA Rennes France Federal Institute of Brasilia Brasilia Brazil

Point clouds have recently gained interest, especially for real-time applications and for 3D-scanned material, such as is used in autonomous driving, architecture, and engineering, to model real estate for renovation or display. Point clouds are associated with geometry information and attributes such as color. Be the color unique or direction-dependent (in the case of plenoptic point clouds), it reflects the colors observed by cameras displaced around the object. Hence, not only are the viewing references assumed, but the illumination spectrum and illumination geometry is also implicit. We propose a model-centric description of the 3D object, that is independent of the illumination and of the position of the cameras. We want to be able to describe the objects themselves such that, at a later stage, the rendering of the model may decide where to place illumination, from which it may calculate the image viewed by a given camera. We want to be able to describe transparent or translucid objects, mirrors, fishbowls, fog and smoke. Volumetric clouds may allow us to describe the air, however "empty", and introduce air particles, in a manner independent of the viewer position. For that, we rely on some eletromagnetic properties to arrive at seven attributes per voxel that would describe the material and its color or transparency. Three attributes are for the transmissivity of each color, three are for the attenuation of each color, and another attribute is for diffuseness. These attributes give information about the object to the renderer, with whom lies the decision on how to render and depict each object. © 2021, CC BY.

关键词： Color

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Development of automatic rice plant pest detection system based on convolutional neural network

引用

AIP Conference Proceedings 2023年第1期2482卷

作者： Matthew Sjarah Winda Astuti Lie Zener Ari Fadli 1)Automotive and Robotics Program Computer Engineering Department BINUS ASO School of Engineering Bina Nusantara University Jakarta Indonesia 11480 2)Electrical Departement Faculty of Engineering Jenderal Soedirman University Purwokerto Jawa Tengah Indonesia 53122

Rice is the most important food crop in Indonesia, one of the plants that is quite significant and as a daily staple food for the people of Indonesia. The Indonesia government through the ministry of Agriculture tries to maintain the rice production by maintaining of the rice field. One the challenging situation that can involve the rice production is pest. Pests became one of the reasons which can reduce rice production. The decrease in rice production due to pest attack is an important problem in rice plant care. In this work, a pest detection system in rice plants developed using an intelligent system technique. The system involved image processing and intelligent technique. The system recognizes the kind of pests of the rice plant based on the feature of the image of the pets. Rice plant pest detection systems based on the pest image proceed by image procession technique and Convolution Neural Network (CNN). The system is working properly, since it resulting the training and testing accuracy of 99% and 90%, respectively.

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A Fog Computing Framework for Cognitive Portable Ground Penetrating Radars

A Fog Computing Framework for Cognitive Portable Ground Pene...

引用

2019 IEEE International Conference on Communications, ICC 2019

作者： Wu, Dalei Omwenga, Maxwell M. Liang, Yu Yang, Li Huston, Dryver Xia, Tian Department of Computer Science and Engineering University of Tennessee at Chattanooga United States Department of Mechanical Engineering University of Vermont United States Department of Electrical and Biomedical Engineering University of Vermont United States

ISBN: (纸本)9781538680889

With distributed communication, computation, and storage resources close to end users/devices, fog computing (FC) makes it very promising to develop cognitive portable ground penetrating radars (GPRs) operating intelligently and adaptively under varying sensing conditions. However both strict performance requirement and tradeoffs between communication and computation pose significant challenges. This paper presents a fog computing framework for cognitive portable GPRs. Specifically, the system architecture of an FC-enabled cognitive portable GPR is developed. Based on the identification of various involved computation tasks, an offloading policy was proposed to determine whether computation tasks should be executed locally or offloaded to the fog server. Experimental results show the efficacy of the proposed methods. The framework also provides insight into the design of cognitive Internet of things (IoT) supported by fog computing. © 2019 IEEE.

关键词： Fog computing

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