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GardnerNet: An Interpretable Deep Learning Model for Quantitative Human Blastocyst Evaluation - A Retrospective Model Development and Validation Study

SSRN

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SSRN 2024年

作者： Liu, Hang Chen, Longbin Shan, Guanqiao Sun, Chen Liao, Hongqing Lu, Changfu Zhang, Shuoping Dong, Shaonan Xu, Xinxin Yan, Qiuyun Gong, Fei Zhang, Zhuoran Dai, Changsheng Chen, Wenyuan Song, Haocong Chen, Lei Sun, Haixiang Wang, Shanshan Lin, Ge Sun, Yu Gu, Yifan Department of Mechanical and Industrial Engineering University of Toronto 5 King’s College Road TorontoONM5S 3G8 Canada Institute of Reproductive and Stem Cell Engineering School of Basic Medical Science Central South University 172 Tongzipo Road Hunan Province Changsha China Hengyang Nanhua-Xinghui Reproductive Health Hospital 30 Jiefang Road Hunan Province Hengyang China The second Affiliated Hospital Hengyang Medical School University of South China 30 Jiefang Road Hunan Province Hengyang City China Reproductive & Genetic Hospital of CITIC-Xiangya 567 Tongzipo Road Hunan Province Changsha China NHC Key Laboratory of Human Stem Cell and Reproductive Engineering School of Basic Medical Science Central South University 172 Tongzipo Road Hunan Province Changsha China Clinical Research Center for Reproduction and Genetics in Hunan Province Reproductive & Genetic Hospital of CITIC-Xiangya 567 Tongzipo Road Hunan Province Changsha China School of Science and Engineering The Chinese University of Hong Kong-Shenzhen 2001 Longxiang Boulevard Longgang District Shenzhen China Institute of Robotics and Intelligent Systems Dalian University of Technolgy 2 Linggong Road Liaoning Province Dalian China Department of Computer Science University of Toronto 40 St George Street TorontoONM5S 2E4 Canada Center for Reproductive Medicine and Obstetrics and Gynecology Nanjing Drum Tower Hospital Nanjing University Medical School 321 Zhongshan Road Jiangsu Province Nanjing China National Engineering and Research Center of Human Stem Cell 8 Luyun Road Jiangsu Province Changsha China Department of Electrical and Computer Engineering 10 King’s College Road TorontoONM5S 3G8 Canada Institute of Biomedical Engineering University of Toronto 164 College Street TorontoONM5S 3G9 Canada

Background: Selecting the blastocyst with the highest potential for live birth from a cohort is crucial for the success of in vitro fertilization (IVF). While the Gardner and Schoolcraft morphological grading system, commonly known as the Gardner grading system, is widely used by embryologists worldwide, blastocysts having similar or the same inner cell mass (ICM) and trophectoderm (TE) grades cause challenges for embryologists in decision-making. Furthermore, human assessment is subjective and inconsistent in predicting which blastocysts have higher potential to result in live birth. This study aims to develop an interpretable, quantitative method aligned with the Gardner grading system, providing continuous scores that correlate with live birth outcomes, thereby assisting embryologists in improving blastocyst selection. Methods: In this retrospective study, we developed GardnerNet, an interpretable deep learning model that outputs expansion degree grade and continuous scores quantifying a blastocyst's ICM morphology and TE morphology, based on the Gardner grading system. The continuous ICM and TE scores were calculated by weighting each base grade's predicted probability and summing the predicted probabilities. To represent each blastocyst's overall potential for live birth, we combined the ICM and TE scores using their odds ratios (ORs) for live birth. We further assessed the correlation between the ICM score, TE score, and the OR-combined score (adjusted for expansion degree) with live birth rates by applying GardnerNet to blastocysts with known live birth outcomes. To test its generalizability, we also applied GardnerNet to an external IVF institution, accounting for variations in imaging conditions, live birth rates, and embryologists' experience levels. Findings: GardnerNet was developed using data from 2760 blastocysts with majority-voted grades for expansion degree, ICM, and TE. The model achieved mean area under the receiver operating characteristic curve

关键词： Risk assessment

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An Open-Source Knowledge Graph Ecosystem for the Life Sciences

arXiv

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arXiv 2023年

作者： Callahan, Tiffany J. Tripodi, Ignacio J. Stefanski, Adrianne L. Cappelletti, Luca Taneja, Sanya B. Wyrwa, Jordan M. Casiraghi, Elena Matentzoglu, Nicolas A. Reese, Justin Silverstein, Jonathan C. Hoyt, Charles Tapley Boyce, Richard D. Malec, Scott A. Unni, Deepak R. Joachimiak, Marcin P. Robinson, Peter N. Mungall, Christopher J. Cavalleri, Emanuele Fontana, Tommaso Valentini, Giorgio Mesiti, Marco Gillenwater, Lucas A. Santangelo, Brook Vasilevsky, Nicole A. Hoehndorf, Robert Bennett, Tellen D. Ryan, Patrick B. Hripcsak, George Kahn, Michael G. Bada, Michael Baumgartner, William A. Hunter, Lawrence E. Computational Bioscience Program University of Colorado Anschutz Medical Campus AuroraCO80045 United States Department of Biomedical Informatics Columbia University Irving Medical Center New YorkNY10032 United States Computer Science Department Interdisciplinary Quantitative Biology University of Colorado Boulder BoulderCO80301 United States AnacletoLab Computer Science Department University of Milan 20122 Italy Intelligent Systems Program University of Pittsburgh PittsburghPA15260 United States Department of Physical Medicine and Rehabilitation School of Medicine University of Colorado Anschutz Medical Campus AuroraCO80045 United States Division of Environmental Genomics and Systems Biology Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Semanticly Ltd Athens Greece Department of Biomedical Informatics University of Pittsburgh School of Medicine PittsburghPA15206 United States Laboratory of Systems Pharmacology Harvard Medical School BostonMA02115 United States Division of Translational Informatics University of New Mexico School of Medicine AlbuquerqueNM87131 United States SIB Swiss Institute of Bioinformatics Basel Switzerland Berlin Institute of Health at Charité-Universitatsmedizin Berlin10117 Germany ELLIS European Laboratory for Learning and Intelligent Systems Germany Department of Biomedical Informatics University of Colorado School of Medicine AuroraCO80045 United States Data Collaboration Center Critical Path Institute 1840 E River Rd. Suite 100 TucsonAZ85718 United States Computer Electrical and Mathematical Sciences & Engineering Division Computational Bioscience Research Center King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia Department of Pediatrics University of Colorado School of Medicine AuroraCO80045 United States Janssen Research and Development RaritanNJ08869 United States Division of General Internal Medicine University of Colorado School of Medicine A

Translational research requires data at multiple scales of biological organization. Advancements in sequencing and multi-omics technologies have increased the availability of these data, but researchers face significant integration challenges. Knowledge graphs (KGs) are used to model complex phenomena, and methods exist to construct them automatically. However, tackling complex biomedical integration problems requires flexibility in the way knowledge is modeled. Moreover, existing KG construction methods provide robust tooling at the cost of fixed or limited choices among knowledge representation models. PheKnowLator (Phenotype Knowledge Translator) is a semantic ecosystem for automating the FAIR (Findable, Accessible, Interoperable, and Reusable) construction of ontologically grounded KGs with fully customizable knowledge representation. The ecosystem includes KG construction resources (e.g., data preparation APIs), analysis tools (e.g., SPARQL endpoints and abstraction algorithms), and benchmarks (e.g., prebuilt KGs and embeddings). We evaluated the ecosystem by systematically comparing it to existing open-source KG construction methods and by analyzing its computational performance when used to construct 12 large-scale KGs. With flexible knowledge representation, PheKnowLator enables fully customizable KGs without compromising performance or usability. © 2023, CC BY.

关键词： Ecosystems

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CNN-based Broad Learning with Efficient Incremental Reconstruction Model for Facial Emotion Recognition ⁎

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IFAC-PapersOnLine 2020年第2期53卷 10236-10241页

作者： Luefeng Chen Min Li Xuzhi Lai Kaoru Hirota Witold Pedrycz School of Automation China University of Geosciences Wuhan 430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074 China Tokyo Institute of Technology Yokohama 226-8502 Japan Department of Electrical and Computer Engineering University of Alberta Edmonton AB T6R 2G7 Canada

Convolutional neural network-based broad learning with efficient incremental reconstruction model (CNNBL) is proposed to recognize emotions in human-robot interaction. It aims to extract deep and abstract features from facial emotional images, and reduce the influence of the complex structure and slow network updates on facial emotion recognition in deep learning. Feature extraction is carried out by convolution and maximum pooling, and then the ridge regression algorithm is used for emotion recognition. When the network needs to expand, the network is dynamically updated by incremental learning algorithm. We verified the experimental performance through k -fold cross validation. According to the recognition results, the accuracy on JAFFE database of our proposal is greater than that of the state of the art, such as the Local Binary Patterns with Softmax and Deep Attentive Multi-path convolutional neural network.

关键词： Convolution Neural Networks Broad Learning Emotion Recognition Human-Robot Interaction

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A registration-aided domain adaptation network for 3D Point cloud based place recognition

arXiv

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arXiv 2020年

作者： Qiao, Zhijian Hu, Hanjiang Shi, Weiang Chen, Siyuan Liu, Zhe Wang, Hesheng Department of Automation Insititue of Medical Robotics Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Jiao Tong University Shanghai200240 China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology China Department of Mechanical Engineering Carnegie Mellon University United States Department of Computer Science and Technology University of Cambridge United Kingdom

In the field of large-scale SLAM for autonomous driving and mobile robotics, 3D point cloud based place recognition has aroused significant research interest due to its robustness to changing environments with drastic daytime and weather variance. However, it is time-consuming and effort-costly to obtain high-quality point cloud data for place recognition model training and ground truth for registration in the real world. To this end, a novel registration-aided 3D domain adaptation network for point cloud based place recognition is proposed. A structure-aware registration network is introduced to help to learn features with geometric information and a 6-DoFs pose between two point clouds with partial overlap can be estimated. The model is trained through a synthetic virtual LiDAR dataset through GTA-V with diverse weather and daytime conditions and domain adaptation is implemented to the real-world domain by aligning the global features. Our results outperform state-of-the-art 3D place recognition baselines or achieve comparable on the real-world Oxford RobotCar dataset with the visualization of registration on the virtual dataset. Copyright © 2020, The Authors. All rights reserved.

关键词： Three dimensional computer graphics

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Optimize Student Learning via Random Forest-Based Adaptive Narrative Game

Optimize Student Learning via Random Forest-Based Adaptive N...

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IEEE International Conference on Automation Science and engineering (CASE)

作者： Ryan Hare Ying Tang Wei Cui Joleen Liang Department of Electrical and Computer Engineering Rowan University Glassboro USA State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Institute of Smart Education Qingdao Academy of Intelligent Industries Qingdao China Squirrel AI Learning Shanghai China

ISBN: (数字)9781728169040

ISBN: (纸本)9781728169057

This paper presents an adaptive narrative game system that focuses on sequential logic design. The system adapts a random forest machine learning model to estimate a student's current level of domain knowledge relative to the problem presented to him through his game-playing behavior data, such as time taken to find solutions, errors in solutions, and emotional indicators. Hints, prompts, and/or individualized lessons are then offered to the player to guide their learning in a positive and productive direction. Our preliminary pilot study demonstrates that the model can make accurate classifications, from which proper assistance can then be provided to individual students as they play.

关键词： Games Databases Forestry Education Problem-solving Presses Artificial intelligence

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Movement Control and Attitude Adjustment of Climbing Robot on Flexible Surfaces

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2018年第3期65卷 2618-2628页

作者： Wu, Xinyu Liu, Jia Zhou, Yimin Lv, Qin Hu, Chaofang CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems and Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems and Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems and Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems and Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China School of Electrical and Information Engineering Tianjin University Tianjin China

In this paper, a series of control algorithms have been designed for the climbing cloth robot, "Clothbot" to climb vertically. The developed algorithm consists of three parts: a sliding mode control with integral action (SMC with integral action), a linear quadratic regulator, and dead reckoning for semi-autonomous navigation. First, the SMC with integral action is developed for high-precision position tracking and speed tracking of the permanent magnet direct current (PMDC) motor installed on the robot. The error between the expected velocity and the actual velocity of the PMDC motor is adopted to design the sliding surface. In order to compensate the fluctuation of the load torque due to the varied friction during climbing, an observer is formulated to estimate the load torque in real-time. Second, the LQR is employed for attitude adjustment by an active torque so as to prevent the robot from falling or being jammed during the movement. Third, due to the onboard-limited energy, the robot is just equippedwith semi-autonomous navigation named dead reckoning carrying on primary localization and primary path planning. Experiments are performed to verify the efficacy of the proposed control algorithms.

关键词： Climbing robot flexible surface linear quadratic regulator load observer sliding mode control with integral action

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Cascade Terminal Sliding Mode Control of a Deployable Cable Driven Robot

Cascade Terminal Sliding Mode Control of a Deployable Cable ...

引用

International Conference on Control, Instrumentation, and Automation

作者： S. A. Khalilpour R. Khorrambakht M. J. Harandi H. D. Taghirad Philippe Cardou Advanced Robotics and Automated Systems Faculty of Electrical and Computer Engineering K.N. Toosi University of Technology Tehran Iran Department of Mechanical Engineering Robotics Laboratory Laval University Quebec City QC Canada

ISBN: (数字)9781728158150

ISBN: (纸本)9781728158167

Most control systems used for cable-driven parallel manipulators employ a simple inner control loop for controlling the driving force of the actuators. By this means the effects of some nonlinear uncertainties of the actuator and power transmission systems are significantly reduced, and in turn, this helps the outer user-specified position control loop to perform more accurately. However, this positive impact on performance relies on non achievable assumptions that the inner control loop is fast and accurate enough, and its dynamics can be totally ignored. The main contribution of this paper is to analyze the stability of the system as a whole, considering both inner and outer loop controllers. The outer loop controller proposed in here is a finite time robust sliding mode whose stability is analyzed through the Lyapunov direct method. A deployable cable-driven parallel robot, as the case study, is also considered in this paper, which is characterized by several intrinsic kinematic and dynamic uncertainties. Finally, the performance and effectiveness of the proposed robust controller is evaluated through some simulations and experiments in order to verify the effectiveness and the characteristics of the cascade controller structure in practice.

关键词： Robots Mathematical model Actuators Jacobian matrices Force Uncertainty Kinematics

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MONAI: An open-source framework for deep learning in healthcare

arXiv

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arXiv 2022年

作者： Cardoso, M. Jorge Li, Wenqi Brown, Richard Ma, Nic Kerfoot, Eric Wang, Yiheng Murrey, Benjamin Myronenko, Andriy Zhao, Can Yang, Dong Nath, Vishwesh He, Yufan Xu, Ziyue Hatamizadeh, Ali Zhu, Wentao Liu, Yun Zheng, Mingxin Tang, Yucheng Yang, Isaac Zephyr, Michael Hashemian, Behrooz Alle, Sachidanand Darestani, Mohammad Zalbagi Budd, Charlie Modat, Marc Vercauteren, Tom Wang, Guotai Li, Yiwen Hu, Yipeng Fu, Yunguan Gorman, Benjamin Johnson, Hans Genereaux, Brad Erdal, Barbaros S. Gupta, Vikash Diaz-Pinto, Andres Dourson, Andre Maier-Hein, Lena Jaeger, Paul F. Baumgartner, Michael Kalpathy-Cramer, Jayashree Flores, Mona Kirby, Justin Cooper, Lee A.D. Roth, Holger R. Xu, Daguang Bericat, David Floca, Ralf Zhou, S. Kevin Shuaib, Haris Farahani, Keyvan Maier-Hein, Klaus H. Aylward, Stephen Dogra, Prerna Ourselin, Sebastien Feng, Andrew School of Biomedical Engineering & Imaging Sciences King’s College London London United Kingdom NVIDIA Corporation Santa Clara and Bethesda United States School of Electrical and Computer Engineering Rice University Houston United States Department of Engineering Science University of Oxford Oxford United Kingdom Department of Medical Physics and Biomedical Engineering University College London London United Kingdom Department of BioAI InstaDeep Ltd London United Kingdom Department of Electrical & Computer Engineering University of Iowa Iowa City United States Mayo Clinic Jacksonville United States Mars Incorporated United States Div. Intelligent Medical Systems German Cancer Research Center Heidelberg Germany Division of Medical Image Computing German Cancer Research Center Heidelberg Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany School of Biomedical Engineering Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou China Department of Medical Physics Guy’s & St Thomas’ NHS Foundation Trust London United Kingdom National Cancer Institute Bethesda United States Kitware Inc. Clifton Park United States Department of Ophthalmology University of Colorado Aurora United States Frederick National Laboratory for Cancer Research Frederick United States Department of Pathology Northwestern University Chicago United States

Artificial Intelligence (AI) is having a tremendous impact across most areas of science. Applications of AI in healthcare have the potential to improve our ability to detect, diagnose, prognose, and intervene on human disease. For AI models to be used clinically, they need to be made safe, reproducible and robust, and the underlying software framework must be aware of the particularities (e.g. geometry, physiology, physics) of medical data being processed. This work introduces MONAI, a freely available, community-supported, and consortium-led PyTorch-based framework for deep learning in healthcare. MONAI extends PyTorch to support medical data, with a particular focus on imaging, and provide purpose-specific AI model architectures, transformations and utilities that streamline the development and deployment of medical AI models. MONAI follows best practices for software-development, providing an easy-to-use, robust, well-documented, and well-tested software framework. MONAI preserves the simple, additive, and compositional approach of its underlying PyTorch libraries. MONAI is being used by and receiving contributions from research, clinical and industrial teams from around the world, who are pursuing applications spanning nearly every aspect of healthcare. © 2022, CC BY.

关键词： Clinical research

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Predictive Inverse Kinematics: optimizing Future Trajectory through Implicit Time Integration and Future Jacobian Estimation

Predictive Inverse Kinematics: optimizing Future Trajectory ...

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2019 IEEE/RSJ International Conference on intelligent Robots and systems (IROS)

作者： Ko Ayusawa Wael Suleiman Eiichi Yoshida CNRS-AIST JRL (Joint Robotics Laboratory) UMI13218/RL Intelligent Systems Research Institute National Institute of Advanced Industrial Science and Technology (IS-AIST) Japan Electrical and Computer Engineering Department Faculty of Engineering University of Sherbrooke Canada

This paper presents an inverse kinematics (IK) method which can control future velocities and accelerations for multi-body systems. The proposed IK method is formulated as a quadratic programing (QP) that optimizes future joint trajectories. The features of the proposed IK are: (1) the evaluation of accelerations at future time instances, (2) the trajectory representation that can implicitly integrate the time integral formula into QP, (3) the computation of future Jacobian matrices based on the comprehensive theory of differential kinematics proposed in our previous work. Those features enable a stable and fast IK computation while evaluating the future accelerations. We also conducted thorough numerical studies to show the efficiency of the proposed method.

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Experimental demonstration of an electrically tunable broadband coherent perfect absorber based on a graphene–electrolyte–graphene sandwich structure

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Photonics Research 2019年第8期7卷 868-874页

作者： JIN ZHANG XINGZHAN WEI MALIN PREMARATNE WEIREN ZHU Department of Electronic Engineering Shanghai Jiao Tong UniversityShanghai200240China Chongqing Institute of Green and Intelligent Technology Chinese Academy of SciencesChongqing400714China Advanced Computing and Simulation Laboratory(AχL) Department of Electrical and Computer Systems EngineeringMonash UniversityClaytonVictoria3800Australia

We propose and experimentally demonstrate the operation of an electrically tunable, broadband coherent perfect absorption(CPA) at microwave frequencies by harnessing the CPA features of a graphene–electrolyte–graphene sandwich structure(GSS). Using both a simplified lumped circuit model and full-wave numerical simulation, it is found that the microwave coherent absorptivity of the GSS can be tuned dynamically from nearly 50% to 100% by changing the Fermi level of the graphene. Strikingly, our simplified lumped circuit model agrees very well with the full-wave numerical model, offering valuable insight into the CPA operation of the device. The angle dependency of coherent absorption in the GSS is further investigated, making suggestions for achieving CPA at wide angles up to 80°. To show the validity and accuracy of our theory and numerical simulations, a GSS prototype is fabricated and measured in a C-band waveguide system. The reasonably good agreement between the experimental and the simulated results confirms that the tunable coherent absorption in GSS can be electrically controlled by changing the Fermi level of the graphene.

关键词： GSS electrolyte Experimental demonstration

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