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SSRN 2023年

作者： Jia, Zhen Luo, Yaguang Wang, Dayang Holliday, Emma Sharma, Arnav Green, Madison M. Roche, Michelle R. Thompson-Witrick, Katherine Flock, Genevieve Pearlstein, Arne J. Yu, Hengyong Zhang, Boce Food Science and Human Nutrition Department University of Florida GainesvilleFL32611 United States Environmental Microbial and Food Safety Lab and Food Quality Lab U.S. Department of Agriculture Agricultural Research Service BeltsvilleMD20705 United States Department of Electrical and Computer Engineering University of Massachusetts LowellMA01854 United States School of Medicine Duke University DurhamNC27710 United States Department of Biomedical and Nutritional Sciences University of Massachusetts LowellMA01854 United States US Army Natick Soldier Research Development and Engineering Center NatickMA01760 United States Department of Mechanical Science and Engineering University of Illinois Urbana-Champaign UrbanaIL61801 United States

Global food systems can benefit significantly from real-time monitoring of microbial food safety, a task for which tedious operations, destructive sampling, and inability to monitor multiple pathogens remain challenging. This study reports significant improvements to a paper chromogenic array sensor - machine learning (PCA-ML) methodology sensing concentrations of volatile organic compounds (VOCs) emitted on a species-specific basis by pathogens, by streamlining dye selection, sensor fabrication, database construction, and machine learning and validation. This approach enables noncontact, time-dependent, simultaneous monitoring of multiple pathogens (Listeria monocytogenes, Salmonella, and E. coli O157:H7) at levels as low as 1 log CFU/g with over 90% accuracy. The report provides theoretical and practical frameworks demonstrating that chromogenic response, including limits of detection, depends on time integrals of VOC concentrations. The paper also discusses the potential for implementing PCA-ML in the food supply chain for different food matrices and pathogens, with species- and strain-specific identification. © 2023, The Authors. All rights reserved.

关键词： Supply chains

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Surformer: An Interpretable Pattern-Perceptive Survival Transformer for Cancer Survival Prediction from Histopathology Whole Slide Images

SSRN

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SSRN 2023年

作者： Wang, Zhikang Gao, Qian Yi, Xiaoping Zhang, Xinyu Zhang, Yiwen Zhang, Daokun Liò, Pietro Bain, Christopher Bassed, Richard Li, Shanshan Guo, Yuming Imoto, Seiya Yao, Jianhua Daly, Roger J. Song, Jiangning Xiangya Hospital Central South University Hunan Changsha China Biomedicine Discovery Institute Department of Biochemistry and Molecular Biology Monash University Melbourne Australia Faculty of Information Technology Monash University Melbourne Australia Climate Air Quality Research Unit School of Public Health and Preventive Medicine Monash University Melbourne Australia Department of Computer Science and Technology The University of Cambridge Cambridge United Kingdom Victorian Institute of Forensic Medicine Melbourne Australia Human Genome Center Institute of Medical Science The University of Tokyo Japan Tencent AI Lab Tencent Shenzhen China

High-resolution histopathology whole slide images (WSIs) contain abundant valuable information for cancer prognostication. However, most computational pathology methods for survival prediction have weak interpretability and cannot explain the decision-making processes reasonably. To address this issue, we propose a highly interpretable neural network termed pattern-perceptive survival transformer (Surformer, a portmanteau of survival and transformer) for cancer survival prediction from WSIs. Notably, it can quantify specific histological patterns and explicitly interpret the model prediction with statistical analysis, all of which are achieved through bag-level labels without any patch/cell-level auxiliary information. Specifically, the proposed ratio-reserved cross- attention module (RRCA) detects valuable features and quantifies them in the form of ratio factors (rfs), in which process both global and multiple local features are generated. Afterward, multi-head self&cross-attention modules proceed with the computation for feature enhancement against noise. Eventually, the designed disentangling loss function guides multiple local features to focus on distinct patterns, thereby assisting RRCA in achieving more explicit quantification. Extensive benchmarking experiments on five TCGA datasets illustrate that Surformer outperforms existing state-of-the-art methods. In addition, we highlight its excellent interpretability and utility by showing attentive visualizations and analysis. Surformer is expected to be exploited as a useful tool for performing histopathology image data-driven analysis and gaining new insights for interpreting the associations between such images and patient survival states. © 2023, The Authors. All rights reserved.

关键词： Decision making

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A decision aid for the port placement problem in robot-assisted hysterectomy

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Laparoscopic, Endoscopic and Robotic Surgery 2023年第2期6卷 43-56页

作者： Mohammad R.Maddah Jean-Marc Classe Isabelle Jaffre Keith A.Watson Katherine S.Lin Damien Chablat Cedric Dumas Caroline G.L.Cao Department of Biomedical Industrialand Human Factors EngineeringWright State University3640 Colonel Glenn HwyDaytonOHUSA Department of Automatique Productique et InformatiqueIMT Atlantique4 Rue Alfred KastlerNantesFrance Laboratoire des Sciences Du Numerique de Nantes(LS2N) 2 Chemin de La Houssini?ereNantesFrance Centre Rene Gauducheau ICO Institut de Cancerologie de L'Ouest BD Professeur Jacques MonodSaint-HerblainFrance Brethen Center for Surgical Advancement in Robotics and Minimally Invasive Surgery Miami Valley HospitalOne Wyoming StreetDaytonOHUSA Department of Computer Science IMT AtlantiqueTechnopole Brest-IroiseBrestFrance Laboratoire des Sciences et Techniques de L'information de La Communication et de La Connaissance(Lab-STICC) Technopole Brest-IroiseBrestFrance Future Industries Institute STEMUniversity of South AustraliaMawson Lakes BlvdMawson LakesAustralia

Objective:In robot-assisted minimally invasive surgery,proper port positioning ensures that surgical tools have adequate access to the surgical site and avoids mid-surgery collisions of robotic *** date,several guidelines have been proposed for more accurate port ***,challenges remain due to variations in patient morphology,anatomy,and,in particular,organ displacement due to insuf-flation in certain laparoscopic *** objective of this study was to design and develop a de-cision aid for optimal port placement in robot-assisted hysterectomy that accounts for patient variability and organ displacement due to ***:Three components were constructed:a robot model,a patient-specific model,and an opti-mization *** three components were integrated,and the system was verified using four different patients who underwent robotic *** verified,two expert surgeons were asked to evaluate the decision aid for face and construct validity.A usability test was conducted using a torso phantom with target organs located in three different *** expert surgeons performed a simulated hysterectomy task with and without the decision aid to evaluate performance and ***:The optimization algorithm was sensitive to individual differences in anatomy in the four *** surgeons successfully established face and construct *** test results showed a 28%-40% reduction in time to task completion with the optimized ports compared to expert-determined port locations without using the decision ***:The decision aid,based on an individualized patient-specific model,robot model,and optimization algorithm,was shown to be effective at addressing the challenges of displaced organs due to insufflation in robot-assisted *** face and construct validity of the decision aid was established by expert surgeons,while efficiency gains in task performance were demonstrated experim

关键词： Optimization Planning tool Patient model Robotic hysterectomy

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Team assignment for heterogeneous multi-robot sensor coverage through graph representation learning

arXiv

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arXiv 2020年

作者： Reily, Brian Zhang, Hao The Human-Centered Robotics Lab The Department of Computer Science The Colorado School of Mines GoldenCO80401 United States

Sensor coverage is the critical multi-robot problem of maximizing the detection of events in an environment through the deployment of multiple robots. Large multi-robot systems are often composed of simple robots that are typically not equipped with a complete set of sensors, so teams with comprehensive sensing abilities are required to properly cover an area. Robots also exhibit multiple forms of relationships (e.g., communication connections or spatial distribution) that need to be considered when assigning robot teams for sensor coverage. To address this problem, in this paper we introduce a novel formulation of sensor coverage by multi-robot systems with heterogeneous relationships as a graph representation learning problem. We propose a principled approach based on the mathematical framework of regularized optimization to learn a unified representation of the multi-robot system from the graphs describing the heterogeneous relationships and to identify the learned representation's underlying structure in order to assign the robots to teams. To evaluate the proposed approach, we conduct extensive experiments on simulated multi-robot systems and a physical multi-robot system as a case study, demonstrating that our approach is able to effectively assign teams for heterogeneous multi-robot sensor coverage. Copyright © 2020, The Authors. All rights reserved.

关键词： Industrial robots

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Simultaneous view and feature selection for collaborative multi-robot perception

arXiv

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arXiv 2020年

作者： Reily, Brian Zhang, Hao Human-Centered Robotics Lab Department of Computer Science Colorado School of Mines GoldenCO80401 United States

Collaborative multi-robot perception provides multiple views of an environment, offering varying perspectives to collaboratively understand the environment even when individual robots have poor points of view or when occlusions are caused by obstacles. These multiple observations must be intelligently fused for accurate recognition, and relevant observations need to be selected in order to allow unnecessary robots to continue on to observe other targets. This research problem has not been well studied in the literature yet. In this paper, we propose a novel approach to collaborative multi-robot perception that simultaneously integrates view selection, feature selection, and object recognition into a unified regularized optimization formulation, which uses sparsity-inducing norms to identify the robots with the most representative views and the modalities with the most discriminative features. As our optimization formulation is hard to solve due to the introduced non-smooth norms, we implement a new iterative optimization algorithm, which is guaranteed to converge to the optimal solution. We evaluate our approach through a case-study in simulation and on a physical multi-robot system. Experimental results demonstrate that our approach enables effective collaborative perception through accurate object recognition and effective view and feature selection. Copyright © 2020, The Authors. All rights reserved.

关键词： Multipurpose robots

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Global guidance network for breast lesion segmentation in ultrasound images

arXiv

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arXiv 2021年

作者： Xue, Cheng Zhu, Lei Fu, Huazhu Hu, Xiaowei Li, Xiaomeng Zhang, Hai Heng, Pheng-Ann Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong Department of Applied Mathematics and Theoretical Physics University of Cambridge Inception Institute of Artificial Intelligence Abu Dhabi United Arab Emirates Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong Shenzhen People's Hospital Second Clinical College of Jinan University First Affiliated Hospital of Southern University of Science and Technology Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China

Automatic breast lesion segmentation in ultrasound helps to diagnose breast cancer, which is one of the dreadful diseases that affect women globally. Segmenting breast regions accurately from ultrasound image is a challenging task due to the inherent speckle artifacts, blurry breast lesion boundaries, and inhomogeneous intensity distributions inside the breast lesion regions. Recently, convolutional neural networks (CNNs) have demonstrated remarkable results in medical image segmentation tasks. However, the convolutional operations in a CNN often focus on local regions, which suffer from limited capabilities in capturing long-range dependencies of the input ultrasound image, resulting in degraded breast lesion segmentation accuracy. In this paper, we develop a deep convolutional neural network equipped with a global guidance block (GGB) and breast lesion boundary detection (BD) modules for boosting the breast ultrasound lesion segmentation. The GGB utilizes the multi-layer integrated feature map as a guidance information to learn the long-range non-local dependencies from both spatial and channel domains. The BD modules learn additional breast lesion boundary map to enhance the boundary quality of a segmentation result refinement. Experimental results on a public dataset and a collected dataset show that our network outperforms other medical image segmentation methods and the recent semantic segmentation methods on breast ultrasound lesion segmentation. Moreover, we also show the application of our network on the ultrasound prostate segmentation, in which our method better identifies prostate regions than state-of-the-art networks. © 2021, CC BY.

关键词： Deep neural networks

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DCMN+: Dual co-matching network for multi-choice reading comprehension 34

DCMN+: Dual co-matching network for multi-choice reading com...

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34th AAAI Conference on Artificial Intelligence, AAAI 2020

作者： Zhang, Shuailiang Zhao, Hai Wu, Yuwei Zhang, Zhuosheng Zhou, Xi Zhou, Xiang Department of Computer Science and Engineering Shanghai Jiao Tong University Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai China CloudWalk Technology Shanghai China

ISBN: (纸本)9781577358350

Multi-choice reading comprehension is a challenging task to select an answer from a set of candidate options when given passage and question. Previous approaches usually only calculate question-aware passage representation and ignore passage-aware question representation when modeling the relationship between passage and question, which cannot effectively capture the relationship between passage and question. In this work, we propose dual co-matching network (DCMN) which models the relationship among passage, question and answer options bidirectionally. Besides, inspired by how humans solve multi-choice questions, we integrate two reading strategies into our model: (i) passage sentence selection that finds the most salient supporting sentences to answer the question, (ii) answer option interaction that encodes the comparison information between answer options. DCMN equipped with the two strategies (DCMN+) obtains state-of-the-art results on five multi-choice reading comprehension datasets from different domains: RACE, SemEval-2018 Task 11, ROCStories, COIN, MCTest. Copyright © 2020, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词：

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Facial Landmark Localization Robust on the Eyes with Position Regression Network

Facial Landmark Localization Robust on the Eyes with Positio...

引用

International Conference on Ubiquitous Robots and Ambient Intelligence (URAI)

作者： Chanwoong Kwak Jaeyoon Jang Hosub Yoon Department of Computer Software and Engineering Korea University of Science and Technology Daejeon Republic of Korea Human-Machine Interaction Research Group Electronics and Telecommunications Research Institute Daejeon Republic of Korea

ISBN: (数字)9781728157153

ISBN: (纸本)9781728157160

Facial landmark localization is essential for robot-human interaction. In particular, the human eye is more important because it can grasp a person's interests. However, the traditional method does not consider eye changes from the dataset, so the limitation is clear, this paper presents a data augmentation method for acquiring various eye images and a method for creating a robust eye landmark model with 2-stage training. Experiments on augmented 300W-LP datasets show that our method outperforms performance than the previous method.

关键词： Face Training Three-dimensional displays Two dimensional displays Measurement computer vision

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Cross-lingual transferring of pre-trained contextualized language models

arXiv

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arXiv 2021年

作者： Li, Zuchao Parnow, Kevin Zhao, Hai Zhang, Zhuosheng Wang, Rui Utiyama, Masao Sumita, Eiichiro The Department of Computer Science and Engineering Shanghai Jiao Tong University Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Kyoto619-0289 Japan

Though the pre-trained contextualized language model (PrLM) has made a significant impact on NLP, training PrLMs in languages other than English can be impractical for two reasons: other languages often lack corpora sufficient for training powerful PrLMs, and because of the commonalities among human languages, computationally expensive PrLM training for different languages is somewhat redundant. In this work, building upon the recent works connecting cross-lingual model transferring and neural machine translation, we thus propose a novel cross-lingual model transferring framework for PrLMs: TRELM. To handle the symbol order and sequence length differences between languages, we propose an intermediate "TRILayer" structure that learns from these differences and creates a better transfer in our primary translation direction, as well as a new cross-lingual language modeling objective for transfer training. Additionally, we showcase an embedding aligning that adversarially adapts a PrLM's non-contextualized embedding space and the TRILayer structure to learn a text transformation network across languages, which addresses the vocabulary difference between languages. Experiments on both language understanding and structure parsing tasks show the proposed framework significantly outperforms language models trained from scratch with limited data in both performance and efficiency. Moreover, despite an insignificant performance loss compared to pre-training from scratch in resource-rich scenarios, our cross-lingual model transferring framework is significantly more economical. Copyright © 2021, The Authors. All rights reserved.

关键词： Embeddings

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A Multi-Task Mean Teacher for Semi-Supervised Shadow Detection

A Multi-Task Mean Teacher for Semi-Supervised Shadow Detecti...

引用

Conference on computer Vision and Pattern Recognition (CVPR)

作者： Zhihao Chen Lei Zhu Liang Wan Song Wang Wei Feng Pheng-Ann Heng College of Intelligence and Computing Tianjin University Department of Computer Science and Engineering The Chinese University of Hong Kong Department of Computer Science and Engineering University of South Carolina Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

Existing shadow detection methods suffer from an intrinsic limitation in relying on limited labeled datasets, and they may produce poor results in some complicated situations. To boost the shadow detection performance, this paper presents a multi-task mean teacher model for semi-supervised shadow detection by leveraging unlabeled data and exploring the learning of multiple information of shadows simultaneously. To be specific, we first build a multi-task baseline model to simultaneously detect shadow regions, shadow edges, and shadow count by leveraging their complementary information and assign this baseline model to the student and teacher network. After that, we encourage the predictions of the three tasks from the student and teacher networks to be consistent for computing a consistency loss on unlabeled data, which is then added to the supervised loss on the labeled data from the predictions of the multi-task baseline model. Experimental results on three widely-used benchmark datasets show that our method consistently outperforms all the compared state-of- the-art methods, which verifies that the proposed network can effectively leverage additional unlabeled data to boost the shadow detection performance.

关键词： Task analysis Image edge detection Feature extraction Training Radio frequency Fuses Solid modeling

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