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Automatic anatomical labeling of arteries and veins using conditional random fields

INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY 2017年第6期12卷 1041-1048页

作者： Kitasaka, Takayuki Kagajo, Mitsuru Nimura, Yukitaka Hayashi, Yuichiro Oda, Masahiro Misawa, Kazunari Mori, Kensaku Aichi Inst Technol Sch Informat Sci Yakusa Cho Toyota 4700392 Japan Nagoya Univ Grad Sch Informat Sci Chikusa Ku Furo Cho Nagoya Aichi 4648601 Japan Nagoya Univ Informat & Commun Chikusa Ku Furo Cho Nagoya Aichi 4648601 Japan Aichi Canc Ctr Hosp Dept Gastroenterol Surg Chikusa Ku Nagoya Aichi 4648681 Japan

For safe and reliable laparoscopic surgery, it is important to determine individual differences of blood vessels such as the position, shape, and branching structures. Consequently, a computer-assisted laparoscopy that displays blood vessel structures with anatomical labels would be extremely beneficial. This paper details an automated anatomical labeling method for abdominal arteries and veins extracted from 3D CT volumes. The proposed method represents a blood vessel tree as a probabilistic graphical model by conditional random fields (CRFs). An adaptive gradient algorithm is adopted for structure learning. The anatomical labeling of blood vessel branches is performed by maximum a posteriori estimation. We applied the proposed method to 50 cases of arterial and portal phase abdominal X-ray CT volumes. The experimental results showed that the F-measure of the proposed method for abdominal arteries and veins was 94.4 and 86.9%, respectively. We developed an automated anatomical labeling method to annotate each blood vessel branches of abdominal arteries and veins using CRF. The proposed method outperformed a state-of-the-art method.

关键词： Anatomical labeling Structure learning probabilistic graphical model Abdomen

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Texas rural land market integration: A causal analysis using machine learning applications

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MACHINE LEARNING WITH APPLICATIONS 2024年 18卷

作者： Su, Tian Dharmasena, Senarath Leatham, David Gilliland, Charles Texas A&M Univ Texas Real Estate Res Ctr 2115 TAMU College Stn TX 77843 USA Texas A&M Univ Dept Agr Econ 2124 TAMU College Stn TX 77843 USA

Texas rural land markets have several special features that makes it unique from other rural land markets in the United States. In 2021, Texas agricultural land, including buildings, is valued at $299.88 billion, which is almost 10% of the nation's total agricultural real estate value and 83% of the state's land is categorized as rural. In addition, due to its size and geologic features, Texas' diverse landscape creates complex and widely divergent conditions affecting ownership and marketing of the land. Despite this complexity, lack of granular level and reliable transactional data on land sales has prevented thorough investigation into Texas land markets to uncover various interdependencies. Using quarterly transactional land value data from 1966 to 2017, this study uses cutting-edge machine learning algorithms and probabilistic graphical models to uncover causal interaction patterns of different land markets in Texas. The results reveal that Texas rural land markets are interdependent. Current and potential landholders and lenders can use the results from this work to aid strategic decision making. Financial institutions and investment groups could be made aware of the trend of one land market relative to other markets and adjust their holdings accordingly. Landowners may better understand changes in net wealth, which affect their ability to borrow capital and operate efficiently. Moreover, lenders may also benefit from the information to manage collateral and thus maintain the stability of their operation.

关键词： Texas rural land markets Machine learning probabilistic graphical model Directed acyclic graphs

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Markov Random Fields for Basic Scene Analysis of Occupancy Grids

Markov Random Fields for Basic Scene Analysis of Occupancy G...

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Global OCEANS Singapore - U.S. Gulf Coast Conference

作者： Kaplan, Michael Bronstein, Daniel Wettergren, Thomas A. Naval Undersea Warfare Ctr Newport RI 02841 USA

ISBN: (纸本)9781728154466

We develop an approach to updating the representation of the environment in near proximity to a platform based upon limited-quality onboard sensor readings. Our method builds upon occupancy grid mapping techniques, and uses a Markov random field as the probabilistic modeling paradigm for resolving discrepancies between neighboring cells in the occupancy grid. This approach is readily implemented with intermittent data of obstacle reports, which is a behavior typically encountered when using forward look sonars operating on unmanned underwater vehicles. We demonstrate the approach through simulation studies of sonar performance in environments with multiple obstacles. Using receiver operator characteristic analysis of the resulting filtering scheme, we show how the method can be tuned to improve performance based upon the expected environmental complexity and sonar performance characteristics. Numerical results from the simulations are presented to show how the method compares to conventional Bayesian filtering.

关键词： Computer vision scene analysis Markov random field occupancy mapping probabilistic graphical model vehicle navigation

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Joint model of topics, expertises, activities and trends for question answering Web applications

Joint model of topics, expertises, activities and trends for...

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IEEE/WIC/ACM International Conference on Web Intelligence (WI)

作者： Meng, Zide Gandon, Fabien Zucker, Catherine Faron INRIA SAM Sophia Antipolis France Univ Nice Sophia Antipolis Nice France

ISBN: (纸本)9781509044702

Users in question-answer sites generate huge amounts of high quality and highly reusable information. This information can be categorized by topics but since users' interests change with time, uncovering the temporal patterns and trends in their activity is of prime interest to detect their current expertize. These temporal variations have long remained unexplored in question-answer sites while detecting them enables us to improve tasks such as: question routing, expert recommending and community life-cycle management. In this paper, we propose a generative model of such a community and its dynamics, and we perform experiments with real-world data extracted from the StackOverflow website to confirm the effectiveness of our model to study the users' behaviors and topics dynamics.

关键词： Community Question Answering probabilistic graphical model Question routing Temporal analysis Topic modeling

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Graph algorithms for predicting subcellular localization at the pathway level 28

Graph algorithms for predicting subcellular localization at ...

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28th Pacific Symposium on Biocomputing (PSB)

作者： Magnano, Chris S. Gitter, Anthony Univ Wisconsin Madison Dept Comp Sci Madison WI 53706 USA Morgridge Inst Res Madison WI 53706 USA Harvard Med Sch Ctr Computat Biomed Boston MA 02115 USA Univ Wisconsin Madison Dept Biostat & Med Informat Madison WI USA

ISBN: (纸本)9789811270611

Protein subcellular localization is an important factor in normal cellular processes and disease. While many protein localization resources treat it as static, protein localization is dynamic and heavily influenced by biological context. Biological pathways are graphs that represent a specific biological context and can be inferred from large-scale data. We develop graph algorithms to predict the localization of all interactions in a biological pathway as an edge-labeling task. We compare a variety of models including graph neural networks, probabilistic graphical models, and discriminative classifiers for predicting localization annotations from curated pathway databases. We also perform a case study where we construct biological pathways and predict localizations of human fibroblasts undergoing viral infection. Pathway localization prediction is a promising approach for integrating publicly available localization data into the analysis of large-scale biological data.

关键词： probabilistic graphical model graph neural network spatial proteomics

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Facial Landmark Detection and Tracking for Facial Behavior Analysis 16

Facial Landmark Detection and Tracking for Facial Behavior A...

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ACM International Conference on Multimedia Retrieval (ICMR)

作者： Wu, Yue Rensselaer Polytech Inst 110 8th St Troy NY 12180 USA

ISBN: (纸本)9781450343596

The face is the most dominant and distinct communication tool of human beings. Automatic analysis of facial behavior allows machines to understand and interpret a human's states and needs for natural interactions. This research focuses on developing advanced computer vision techniques to process and analyze facial images for the recognition of various facial behaviors. Specifically, this research consists of two parts: automatic facial landmark detection and tracking, and facial behavior analysis and recognition using the tracked facial landmark points. In the first part, we develop several facial landmark detection and tracking algorithms on facial images with varying conditions, such as varying facial expressions, head poses and facial occlusions. First, to handle facial expression and head pose variations, we introduce a hierarchical probabilistic face shape model and a discriminative deep face shape model to capture the spatial relationships among facial landmark points under different facial expressions and face poses to improve facial landmark detection. Second, to handle facial occlusion, we improve upon the effective cascade regression framework and propose the robust cascade regression framework for facial landmark detection, which iteratively predicts the landmark visibility probabilities and landmark locations. The second part of this research applies our facial landmark detection and tracking algorithms to facial behavior analysis, including facial action recognition and face pose estimation. For facial action recognition, we introduce a novel regression framework for joint facial landmark detection and facial action recognition. For head pose estimation, we are working on a robust algorithm that can perform head pose estimation under facial occlusion.

关键词： Facial behavior analysis Facial landmark detection and tracking probabilistic graphical model

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Towards Practical Federated Causal Structure Learning

Towards Practical Federated Causal Structure Learning

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5th International Workshop on Learning with Imbalanced Domains - Theory and Applications / European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)

作者： Wang, Zhaoyu Ma, Pingchuan Wang, Shuai Hong Kong Univ Sci & Technol Clear Water Bay Hong Kong Peoples R China

ISBN: (纸本)9783031434143;9783031434150

Understanding causal relations is vital in scientific discovery. The process of causal structure learning involves identifying causal graphs from observational data to understand such relations. Usually, a central server performs this task, but sharing data with the server poses privacy risks. Federated learning can solve this problem, but existing solutions for federated causal structure learning make unrealistic assumptions about data and lack convergence guarantees. (FEDCSL)-S-2 is a federated constraint-based causal structure learning scheme that learns causal graphs using a federated conditional independence test, which examines conditional independence between two variables under a condition set without collecting raw data from clients. (FEDCSL)-S-2 requires weaker and more realistic assumptions about data and offers stronger resistance to data variability among clients. FEDPC and FEDFCI are the two variants of (FEDCSL)-S-2 for causal structure learning in causal sufficiency and causal insufficiency, respectively. The study evaluates (FEDCSL)-S-2 using both synthetic datasets and real-world data against existing solutions and finds it demonstrates encouraging performance and strong resilience to data heterogeneity among clients.

关键词： federated learning Bayesian network probabilistic graphical model causal discovery

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Predicting Wikipedia Editor's Editing Interest Based on Factor Graph model 3

Predicting Wikipedia Editor's Editing Interest Based on Fact...

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3rd IEEE International Congress on Big Data

作者： Zhang, Haisu Zhang, Sheng Wu, Zhaolin Huang, Liwei Ma, Yutao Acad Natl Def Informat Dept 1 Wuhan 430010 Peoples R China PLA Univ Sci & Technol Coll Command Informat Syst Nanjing 210007 Jiangsu Peoples R China Wuhan Univ State Key Lab Software Engn Wuhan 430072 Peoples R China Wuhan Iron & Steel Grp Corp WISET Automat Co Ltd Wuhan 430080 Peoples R China

ISBN: (纸本)9781479950577

Recruiting or recommending appropriate latent editors who can edit a specific entry (or called article) plays an important role in improving the quality of Wikipedia entries. To predict an editor's editing interest for Wikipedia entries, this paper proposes an Interest Prediction Factor Graph (IPFG) model, which is characterized by editor's social properties, hyperlinks between Wikipedia entries, categories of an entry and other important features. Furthermore, the paper suggests a parameter learning algorithm based on the gradient descent and Loopy Sum-Product algorithms for factor graphs. The experiment on a Wikipedia dataset shows that, the average prediction accuracy (F1-Measure) of the IPFG model could be up to 87.5%, which is about 35% higher than that of a collaborative filtering approach. Moreover, the paper analyses how incomplete social properties and editing bursts affect the prediction accuracy of the IPFG model. What we found would provide a useful insight into effective Wikipedia article tossing, and improve the quality of those entries that belong to specific categories by means of collective collaboration.

关键词： Factor Graph Interest Prediction Social Network Mining Wikipedia probabilistic graphical model

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BayesPerf: Minimizing Performance Monitoring Errors using Bayesian Statistics 21

BayesPerf: Minimizing Performance Monitoring Errors using Ba...

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26th International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS)

作者： Banerjee, Subho S. Jha, Saurabh Kalbarczyk, Zbigniew Iyer, Ravishankar K. Univ Illinois Urbana IL 61820 USA

ISBN: (纸本)9781450383172

Hardware performance counters (HPCs) that measure low-level architectural and microarchitectural events provide dynamic contextual information about the state of the system. However, HPC measurements are error-prone due to non determinism (e.g., undercounting due to event multiplexing, or OS interrupt-handling behaviors). In this paper, we present BayesPerf, a system for quantifying uncertainty in HPC measurements by using a domain-driven Bayesian model that captures microarchitectural relationships between HPCs to jointly infer their values as probability distributions. We provide the design and implementation of an accelerator that allows for low-latency and low-power inference of the BayesPerf model for x86 and ppc64 CPUs. BayesPerf reduces the average error in HPC measurements from 40.1% to 7.6% when events are being multiplexed. The value of BayesPerf in real-time decision-making is illustrated with a simple example of scheduling of PCIe transfers.

关键词： Performance Counter Sampling Errors Error Detection Error Correction probabilistic graphical model Accelerator

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FAQ: Questions Asked Frequently 16

FAQ: Questions Asked Frequently

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35th ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems (PODS)

作者： Khamis, Mahmoud Abo Ngo, Hung Q. Rudra, Atri LogicBlox Inc New York NY 10011 USA SUNY Buffalo Buffalo NY 14260 USA SUNY Buffalo Comp Sci & Engn Buffalo NY USA

ISBN: (纸本)9781450341912

We define and study the Functional A ggregate Query (FAQ) problem, which encompasses many frequently asked questions in constraint satisfaction, databases, matrix operations, probabilistic graphical models and logic. This is our main conceptual contribution. We then present a simple algorithm called InsideOut to solve this general problem. InsideOut is a variation of the traditional dynamic programming approach for constraint programming based on variable elimination. Our variation adds a couple of simple twists to basic variable elimination in order to deal with the generality of FAQ, to take full advantage of Grohe and Marx's fractional edge cover framework, and of the analysis of recent worst-case optimal relational join algorithms. As is the case with constraint programming and graphical model inference, to make InsideOut run efficiently we need to solve an optimization problem to compute an appropriate variable ordering. The main technical contribution of this work is a precise characterization of when a variable ordering is 'semantically equivalent' to the variable ordering given by the input FAQ expression. Then, we design an approximation algorithm to find an equivalent variable ordering that has the best 'fractional FAQ-width'. Our results imply a host of known and a few new results in graphical model inference, matrix operations, relational joins, and logic. We also briefly explain how recent algorithms on beyond worst-case analysis for joins and those for solving SAT and #SAT can be viewed as variable elimination to solve FAQ over compactly represented input functions.

关键词： Join algorithm Quantified conjunctive query Logic Semiring probabilistic graphical model Constraint satisfaction

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