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Neuromodulation: Technology at the Neural Interface 2022年第7期25卷 S170-S170页

作者： Tom Berfelo Imre Krabbenbos Marloe De Beer Boudewijn Van Den Berg Jan Buitenweg University of Twente Biomedical Signals & Systems Faculty Of Electrical Engineering Mathematics And Computer Science Enschede Netherlands St. Antonius Hospital Anesthesiology Intensive Care And Pain Medicine Nieuwegein Netherlands

Identification of coal types on the basis of analysis of their properties by means of statistical methods 29

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Identification of coal types on the basis of analysis of the...

29th International Mineral Processing Congress, IMPC 2018

作者： Niedoba, T. Pięta, P. Surowiak, A. Jamróz, D. AGH University of Science and Technology Faculty of Mining and Geoengineering Department of Environmental Engineering and Mineral Processing Mickiewicza 30 Krakow30-059 Poland AGH University of Science and Technology Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering Department of Applied Computer Science al. Mickiewicza 30 Krakow30-059 Poland

ISBN: (纸本)9787030227119

The separation of coal material of three types of coals originating from three various Polish hard coal mines (types 31, 34.2 and 35, according to Polish nomenclature, which were steam coal, semi-coking coal and coking coal) into particle size fractions and then into particle density fractions was done and then the following parameters were measured for each particle size-density fraction: combustion heat, ash contents, sulfur contents, volatile parts contents, analytic moisture. In this way a 7-dimensional vector of data was created. Using methods of factor analysis the important features of coal were selected, which decide about their membership to individual types. To evaluate the appropriateness of the applied method the Bartlett's sphericity test as well coefficient of Kaiser-Mayer-Olkin (KMO) were used. To select important factors the Kaiser criterion and Cattell's scree test were used. The obtained results were compared with the results obtained in previous works by means of observation tunnels method. The results showed which particular features are crucial to define the type of coal what is also important to select appropriate method of its enrichment. © IMPC 2018 - 29th International Mineral Processing Congress. All rights reserved.

关键词： Factor analysis

Attention based CNN-LSTM Network for Pulmonary Embolism Prediction on Chest Computed Tomography Pulmonary Angiograms

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arXiv 2021年

作者： Suman, Sudhir Singh, Gagandeep Sakla, Nicole Gattu, Rishabh Green, Jeremy Phatak, Tej Samaras, Dimitris Prasanna, Prateek Dept. of Electrical Engineering Indian Institute of Technology Bombay India Dept. of Radiology Newark Beth Israel Medical Center NJ United States Dept. of Computer Science Stony Brook University NY United States Dept. of Biomedical Informatics Stony Brook University NY United States

With more than 60,000 deaths annually in the United States, Pulmonary Embolism (PE) is among the most fatal cardiovascular diseases. It is caused by an artery blockage in the lung;confirming its presence is time-consuming and is prone to over-diagnosis. The utilization of automated PE detection systems is critical for diagnostic accuracy and efficiency. In this study we propose a two-stage attention-based CNN-LSTM network for predicting PE, its associated type (chronic, acute) and corresponding location (leftsided, rightsided or central) on computed tomography (CT) examinations. We trained our model on the largest available public Computed Tomography Pulmonary Angiogram PE dataset (RSNA-STR Pulmonary Embolism CT (RSPECT) Dataset, N=7279 CT studies) and tested it on an in-house curated dataset of N=106 studies. Our framework mirrors the radiologic diagnostic process via a multi-slice approach so that the accuracy and pathologic sequela of true pulmonary emboli may be meticulously assessed, enabling physicians to better appraise the morbidity of a PE when present. Our proposed method outperformed a baseline CNN classifier and a single-stage CNN-LSTM network, achieving an AUC of 0.95 on the test set for detecting the presence of PE in the study. Copyright © 2021, The Authors. All rights reserved.

关键词： computerized tomography

Adversarial robust training of deep learning MRI reconstruction models

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arXiv 2020年

作者： Calivá, Francesco Cheng, Kaiyang Shah, Rutwik Pedoia, Valentina CI2 Department of Radiology and Biomedical Imaging University of California San Francisco United States Department of Electrical Engineering and Computer Sciences University of California Berkeley United States

Deep Learning (DL) has shown potential in accelerating Magnetic Resonance Image acquisition and reconstruction. Nevertheless, there is a dearth of tailored methods to guarantee that the reconstruction of small features is achieved with high fidelity. In this work, we employ adversarial attacks to generate small synthetic perturbations, which are difficult to reconstruct for a trained DL reconstruction network. Then, we use robust training to increase the network’s sensitivity to these small features and encourage their reconstruction. Next, we investigate the generalization of said approach to real world features. For this, a musculoskeletal radiologist annotated a set of cartilage and meniscal lesions from the knee Fast-MRI dataset, and a classification network was devised to assess the reconstruction of the features. Experimental results show that by introducing robust training to a reconstruction network, the rate of false negative features (4.8%) in image reconstruction can be reduced. These results are encouraging, and highlight the necessity for attention to this problem by the image reconstruction community, as a milestone for the introduction of DL reconstruction in clinical practice. To support further research, we make our annotations and code publicly available here. © 2020, CC BY.

关键词： Magnetic resonance imaging

Sampling-Based Nonlinear Stochastic Optimal Control for Neuromechanical Systems

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Sampling-Based Nonlinear Stochastic Optimal Control for Neur...

Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Emily A. Reed Marcus A. Pereira Francisco J. Valero-Cuevas Evangelos A. Theodorou Ming Hsieh Electrical and Computer Engineering Department University of Southern California Los Angeles CA USA Daniel Guggenheim School of Aerospace Engineering Georgia Institute of Technology Atlanta GA USA Valero-Cuevas is with the Department of Biomedical Engineering University of Southern California Los Angeles CA USA

ISBN: (数字)9781728119908

ISBN: (纸本)9781728119915

Determining how the nervous system controls tendon-driven bodies remains an open question. Stochastic optimal control (SOC) has been proposed as a plausible analogy in the neuroscience community. SOC relies on solving the Hamilton-Jacobi-Bellman equation, which seeks to minimize a desired cost function for a given task with noisy controls. We evaluate and compare three SOC methodologies to produce tapping by a simulated planar 3-joint human index finger: iterative Linear Quadratic Gaussian (iLQG), Model-Predictive Path Integral Control (MPPI), and Deep Forward-Backward Stochastic Differential Equations (FBSDE). We show that averaged over 128 repeats these methodologies can place the fingertip at the desired final joint angles but-because of kinematic redundancy and the presence of noise-they each have joint trajectories and final postures with different means and variances. iLQG in particular, had the largest kinematic variance and departure from the final desired joint angles. We demonstrate that MPPI and FBSDE have superior performance for such nonlinear, tendon-driven systems with noisy *** relevance- The mathematical framework provided by MPPI and FBSDE may be best suited for tendon-driven anthropomorphic robots, exoskeletons, and prostheses for amputees.

关键词： Mathematical model Indexes Tendons Stochastic processes Joints Optimal control Trajectory

A comparison of approaches to improve worst-case predictive model performance over patient subpopulations

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arXiv 2021年

作者： Pfohl, Stephen R. Zhang, Haoran Xu, Yizhe Foryciarz, Agata Ghassemi, Marzyeh Shah, Nigam H. Stanford Center for Biomedical Informatics Research Stanford University StanfordCA94305 United States Department of Computer Science University of Toronto TorontoON Canada Department of Computer Science Stanford University StanfordCA94305 United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Institute for Medical and Evaluative Sciences Massachusetts Institute of Technology CambridgeMA02139 United States

Predictive models for clinical outcomes that are accurate on average in a patient population may underperform drastically for some subpopulations, potentially introducing or reinforcing inequities in care access and quality. Model training approaches that aim to maximize worst-case model performance across subpopulations, such as distributionally robust optimization (DRO), attempt to address this problem without introducing additional harms. We conduct a large-scale empirical study of DRO and several variations of standard learning procedures to identify approaches for model development and selection that consistently improve disaggregated and worst-case performance over subpopulations compared to standard approaches for learning predictive models from electronic health records data. In the course of our evaluation, we introduce an extension to DRO approaches that allows for specification of the metric used to assess worst-case performance. We conduct the analysis for models that predict in-hospital mortality, prolonged length of stay, and 30-day readmission for inpatient admissions, and predict in-hospital mortality using intensive care data. We find that, with relatively few exceptions, no approach performs better, for each patient subpopulation examined, than standard learning procedures using the entire training dataset. These results imply that when it is of interest to improve model performance for patient subpopulations beyond what can be achieved with standard practices, it may be necessary to do so via techniques that implicitly or explicitly increase the effective sample size. Copyright © 2021, The Authors. All rights reserved.

关键词： Optimization

Toward Autonomous Robotic Micro-Suturing using Optical Coherence Tomography Calibration and Path Planning

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Toward Autonomous Robotic Micro-Suturing using Optical Coher...

IEEE International Conference on Robotics and Automation (ICRA)

作者： Yuan Tian Mark Draelos Gao Tang Ruobing Qian Anthony Kuo Joseph Izatt Kris Hauser Department of Electrical and Computer Engineering Duke University Durham NC USA Department of Biomedical Engineering Duke University Durham NC USA Department of Computer Science University of Illinois Urbana-Champaign Urbana IL USA Department of Ophthalmology Duke University Medical Center Durham NC USA

ISBN: (数字)9781728173955

ISBN: (纸本)9781728173962

Robotic automation has the potential to assist human surgeons in performing suturing tasks in microsurgery, and in order to do so a robot must be able to guide a needle with sub-millimeter precision through soft tissue. This paper presents a robotic suturing system that uses 3D optical coherence tomography (OCT) system for imaging feedback. Calibration of the robot-OCT and robot-needle transforms, wound detection, keypoint identification, and path planning are all performed automatically. The calibration method handles pose uncertainty when the needle is grasped using a variant of iterative closest points. The path planner uses the identified wound shape to calculate needle entry and exit points to yield an evenly-matched wound shape after closure. Experiments on tissue phantoms and animal tissue demonstrate that the system can pass a suture needle through wounds with 0.200 mm overall accuracy in achieving the planned entry and exit points, and over 20× more precise than prior autonomous suturing robots.

关键词： Needles Robots Wounds Calibration Surgery Imaging Three-dimensional displays

Roadmap on Deep Learning for Microscopy

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arXiv 2023年

作者： Volpe, Giovanni Wählby, Carolina Tian, Lei Hecht, Michael Yakimovich, Artur Monakhova, Kristina Waller, Laura Sbalzarini, Ivo F. Metzler, Christopher A. Xie, Mingyang Zhang, Kevin Lenton, Isaac C.D. Rubinsztein-Dunlop, Halina Brunner, Daniel Bai, Bijie Ozcan, Aydogan Midtvedt, Daniel Wang, Hao Sladoje, Nataša Lindblad, Joakim Smith, Jason T. Ochoa, Marien Barroso, Margarida Intes, Xavier Qiu, Tong Yu, Li-Yu You, Sixian Liu, Yongtao Ziatdinov, Maxim A. Kalinin, Sergei V. Sheridan, Arlo Manor, Uri Nehme, Elias Goldenberg, Ofri Shechtman, Yoav Moberg, Henrik K. Langhammer, Christoph Špačková, Barbora Helgadottir, Saga Midtvedt, Benjamin Argun, Aykut Thalheim, Tobias Cichos, Frank Bo, Stefano Hubatsch, Lars Pineda, Jesus Manzo, Carlo Bachimanchi, Harshith Selander, Erik Homs-Corbera, Antoni Fränzl, Martin de Haan, Kevin Rivenson, Yair Korczak, Zofia Adiels, Caroline Beck Mijalkov, Mite Veréb, Dániel Chang, Yu-Wei Pereira, Joana B. Matuszewski, Damian Kylberg, Gustaf Sintorn, Ida-Maria Caicedo, Juan C. Cimini, Beth A. Bell, Muyinatu Lediju A. Saraiva, Bruno M. Jacquemet, Guillaume Henriques, Ricardo Ouyang, Wei Le, Trang Gómez-De-Mariscal, Estibaliz Sage, Daniel Muñoz-Barrutia, Arrate Lindqvist, Ebba Josefson Bergman, Johanna Department of Physics University of Gothenburg Gothenburg41296 Sweden Dept. IT and SciLifeLab Uppsala University Sweden Department of Electrical and Computer Engineering Boston University BostonMA02215 United States Department of Biomedical Engineering Boston University BostonMA02215 United States Görlitz Germany Department of Renal Medicine Division of Medicine University College London Royal Free Hospital Campus London United Kingdom Artificial Intelligence for Life Sciences CIC Dorset United Kingdom Roche Pharma International Informatics Roche Diagnostics GmbH Mannheim Germany Department of Electrical Engineering and Computer Sciences UC Berkeley United States Technische Universität Dresden Faculty of Computer Science Dresden Germany Max Planck Institute of Molecular Cell Biology and Genetics Center for Systems Biology Dresden Dresden Germany University of Maryland College Park United States Klosterneuburg3400 Austria School of Mathematics and Physics The University of Queensland Brisbane4072 Australia Australian Research Council Centre of Excellence for Engineered Quantum Systems The University of Queensland Brisbane4072 Australia Département d’Optique P. M. Duffieux Institut FEMTO-ST Université Franche-Comté CNRS UMR 6174 Besançon France Department of Electrical and Computer Engineering University of California Los Angeles Los AngelesCA United States Department of Information Technology Uppsala University Sweden Department of Biomedical Engineering Rensselaer Polytechnic Institute TroyNY12180 United States Elephas 1 Erdman Pl. Madison53705 United States University of Wisconsin–Madison Department of Medical Physics MadisonWI53705 United States Department of Molecular and Cellular Physiology Albany Medical College AlbanyNY12208 United States Research Laboratory of Electronics Electrical Engineering and Computer Science Massachusetts Institute of Technology BostonMA United States Center for Nanophase Materials Sciences Oak R

Through digital imaging, microscopy has evolved from primarily being a means for visual observation of life at the micro- and nano-scale, to a quantitative tool with ever-increasing resolution and throughput. Artificial intelligence, deep neural networks, and machine learning are all niche terms describing computational methods that have gained a pivotal role in microscopy-based research over the past decade. This Roadmap is written collectively by prominent researchers and encompasses selected aspects of how machine learning is applied to microscopy image data, with the aim of gaining scientific knowledge by improved image quality, automated detection, segmentation, classification and tracking of objects, and efficient merging of information from multiple imaging modalities. We aim to give the reader an overview of the key developments and an understanding of possibilities and limitations of machine learning for microscopy. It will be of interest to a wide cross-disciplinary audience in the physical sciences and life sciences. Copyright © 2023, The Authors. All rights reserved.

关键词： Image enhancement

A Vision-Language Foundation Model to Enhance Efficiency of Chest X-ray Interpretation

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arXiv 2024年

作者： Chen, Zhihong Varma, Maya Xu, Justin Paschali, Magdalini Van Veen, Dave Johnston, Andrew Youssef, Alaa Blankemeier, Louis Bluethgen, Christian Altmayer, Stephan Valanarasu, Jeya Maria Jose Muneer, Mohamed Siddig Eltayeb Reis, Eduardo Pontes Cohen, Joseph Paul Olsen, Cameron Abraham, Tanishq Mathew Tsai, Emily B. Beaulieu, Christopher F. Jitsev, Jenia Gatidis, Sergios Delbrouck, Jean-Benoit Chaudhari, Akshay S. Langlotz, Curtis P. Stanford Center for Artificial Intelligence in Medicine and Imaging Stanford University Palo AltoCA United States Department of Radiology Stanford University StanfordCA United States Department of Computer Science Stanford University StanfordCA United States Big Data Institute University of Oxford Oxford United Kingdom Department of Electrical Engineering Stanford University StanfordCA United States Department of Radiology University Hospital Zurich Zürich Switzerland Stability AI London United Kingdom Jülich Supercomputing Centre Jülich Germany LAION Germany Department of Biomedical Data Science Stanford University StanfordCA United States Department of Medicine Stanford University StanfordCA United States

Over 1.4 billion chest X-rays (CXRs) are performed annually due to their cost-effectiveness as an initial diagnostic test. This scale of radiological studies provides a significant opportunity to streamline CXR interpretation and documentation. While foundation models are a promising solution, the lack of publicly available large-scale datasets and benchmarks inhibits their iterative development and real-world evaluation. To overcome these challenges, we constructed a large-scale dataset (CheXinstruct), which we utilized to train a vision-language foundation model (CheXagent). We systematically demonstrated competitive performance across eight distinct task types on our novel evaluation benchmark (CheXbench). Beyond technical validation, we assessed the real-world utility of CheXagent in directly drafting radiology reports. Our clinical assessment with eight radiologists revealed a 36% time saving for residents using CheXagent-drafted reports, while attending radiologists showed no significant time difference editing resident-drafted or CheXagent-drafted reports. The CheXagent-drafted reports improved the writing efficiency of both radiology residents and attending radiologists in 81% and 61% of cases, respectively, without loss of quality. Overall, we demonstrate that CheXagent can effectively perform a variety of CXR interpretation tasks and holds potential to assist radiologists in routine clinical workflows. © 2024, CC BY.

关键词： Visual languages

3D object localization using 2D estimates for computer vision applications

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arXiv 2020年

作者： Siddique, Taha Hasan Masood Rehman, Yawar Rafiq, Tahira Nisar, Malik Zohaib Ibrahim, Muhammad Sohail Usman, Muhammad College of Information Science & Electronic Engineering Zhejiang University Zhejiang Hangzhou310027 China Department of Electronic Engineering NED University of Engineering and Technology Karachi75270 Pakistan Department of Biomedical Engineering Sungkyunkwan University Suwon-16419 Korea Republic of College of Electrical Engineering Zhejiang University Zhejiang Hangzhou310027 China Department of Computer Engineering Chosun University Gwangju-61452 Korea Republic of

—computer vision based applications have received notable attention globally due to the interaction with the physical world. In this paper, a novel method for object localization based on camera calibration and pose estimation is discussed. The 3-dimensional (3D) coordinates are computed by taking multiple 2-dimensional (2D) images from different view that meets the requirement in camera calibration process. Several number of steps are involved in camera calibration including estimation of intrinsic and extrinsic parameters for the removal of lens distortion, estimation of object’s size and camera location. Besides, a technique to estimate the 3D pose using 2D images is proposed and the results of camera parameters and localization are applied for the 3D reconstruction. The hardware implementation of the proposed approach is implemented on HP core i5 with the MATLAB support packages and experimental results are validated for both camera calibration and pose estimation. Copyright © 2020, The Authors. All rights reserved.

关键词： computer vision