Article
Computer Science, Software Engineering
Ye Liu, Shuohong Wang, Jianhui Nie, Hao Gao
Summary: This paper presents a method for detecting and tracking a large number of densely aggregated microbes with arbitrary orientations in image sequences captured under a microscope. By proposing an ICF-based detector and refining detection results through a data association process, the kinematic pattern of microbes is accurately modeled and used to select true targets and match them across frames effectively. Experimental results demonstrate the effectiveness of the proposed method.
Article
Computer Science, Interdisciplinary Applications
Zhenpeng Tang, Yanping Jiang, Feifei Yang
Summary: With the increase in the number of motor vehicles in big cities, the issue of parking difficulties has become more prominent. This study focuses on how to optimize the allocation of shared idle parking spaces to provide intelligent and efficient parking services for parking demanders. A matching model is developed to maximize the utilization of shared parking spaces, and a Lagrangian relaxation algorithm is used to efficiently solve the model. The algorithm shows high computational efficiency and accuracy, especially for large-scale problems.
COMPUTERS & INDUSTRIAL ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Maocan Song, Lin Cheng
Summary: With the increasing demand for travel and limited transportation resources, traffic congestion remains a challenging problem. This study considers the variability of travel times in vehicle routing optimization by utilizing historical travel time data. A mean-standard deviation based vehicle routing model is developed and solved using an augmented Lagrangian relaxation approach. The proposed method effectively reduces the relative gap between the lower and upper bounds in the solving procedure.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Engineering, Civil
Xiaomeng Cao, Jian Lan, X. Rong Li, Yu Liu
Summary: This paper introduces a novel approach for automotive radar-based extended object tracking, which jointly estimates the kinematic state and extension of a vehicle, using a rectangular shape to describe the vehicle and partitioning the area to simplify the scattering center distribution modeling. The proposed method demonstrates its effectiveness through simulated and real data.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Maocan Song, Lin Cheng, Bin Lu
Summary: This study focuses on solving the multi-compartment vehicle routing problem and proposes two new formulations. By applying Lagrangian relaxation and decomposition techniques, high-quality feasible solutions are obtained.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Management
Xavier Cabezas, Sergio Garcia
Summary: This article studies the Simple Plant Location Problem with Order (SPLPO), a variant of the well-studied Simple Plant Location Problem (SPLP). The authors propose a heuristic method based on Lagrangian relaxation and demonstrate its good performance in providing efficient solutions for SPLPO. The article also explores the properties of the SPLPO model and extends them for solving the SPLPO problem.
JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY
(2023)
Article
Chemistry, Analytical
Abhijeet Boragule, Hyunsung Jang, Namkoo Ha, Moongu Jeon
Summary: This study introduces a pixel-guided method to efficiently build a joint detection and tracking framework for multi-object tracking. By queuing and utilizing per-pixel distributions to compute the association matrix, and introducing long-term appearance association in track features, advanced MOT performance is achieved.
Article
Transportation Science & Technology
Xiaoming Xu, Yanhong Yu, Jiancheng Long
Summary: Vehicle timetabling and scheduling in a public transit system are usually performed separately, resulting in a lack of trade-off between bus timetables and vehicle schedules. This paper proposes an integrated framework for electric bus timetabling and scheduling, considering various factors such as headway times, depot requirements, deadheading, and vehicle battery capacities. A time-space network is constructed with inventory arcs to decrease the network size, and a multi-commodity network flow model is formulated. Through a Lagrangian relaxation heuristic, the proposed method efficiently produces bus timetables and schedules with improved profit and valid bounds.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2023)
Article
Computer Science, Hardware & Architecture
Chang Liu, Dong Wang, Chunjuan Bo
Summary: Great success has been achieved in short-term visual object tracking, but long-term tracking is still lacking attention, especially for target disappearances and reappearances. This paper proposes a long-term tracking framework that combines a short-term local tracker and a high-quality global re-detection and distractor association mechanisms. The framework effectively handles target relocation and avoids incorrect target relocation after disappearance. Experimental results on benchmark datasets show that the proposed framework performs well compared to other competing algorithms.
COMPUTERS & ELECTRICAL ENGINEERING
(2023)
Article
Engineering, Civil
Zhaojin Li, Haoxun Chen, Ya Liu, Kun Jin
Summary: This study investigates a specific problem faced by logistics companies in a competitive market and proposes a solution. The study uses a bi-objective model and performs numerical experiments and a case study, demonstrating the effectiveness of the proposed method in practical applications.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Nanoscience & Nanotechnology
Danni Chen, Heng Li, Bin Yu, Junle Qu
Summary: This study proposes a method for researching dynamic events in living cells by simultaneously monitoring spatial positions and changes in local environment related to fluorescence lifetime. The feasibility of the method is verified through experiments, and intracellular endocytosis in a living cell is successfully observed.
Article
Computer Science, Artificial Intelligence
Yuan Xu, Youyuan Chen, Yang Zhang, Qunxiong Zhu, Yanlin He, Hao Sheng
Summary: Multi-object tracking is an important branch of computer vision used for behavior recognition and event analysis. Most research currently focuses on tracking accuracy, with a lack of research on real-time performance. The proposed Bilateral Association Tracking (BAT) framework uses tracklet as the basic node for tracking and introduces a Parzen density based Hierarchical Agglomerative Clustering (P-HAC) algorithm for generating high confidence tracklets. The Dual Appearance Features (DAF) approach considers both spatial and temporal features, improving tracklet association accuracy. BAT outperforms Deepsort in association accuracy and trajectory integrity without obvious efficiency decline, showing significant advantage on computational cost compared to other state-of-the-art trackers while maintaining competitive tracking accuracy. This research aims to promote real-time tracking applications in the future.
IET IMAGE PROCESSING
(2023)
Article
Automation & Control Systems
Hui Li, Yapeng Liu, Xiaoguo Liang, Yongfeng Yuan, Yuanzhi Cheng, Guanglei Zhang, Shinichi Tamura
Summary: This paper proposes a tracking-by-detection framework for multi-object tracking (MOT) that detects objects in each frame and identifies associations with objects in the previous frame. A deep association network is used to match object features and calculate associations to achieve accurate tracking. The framework addresses the problem of missing and partial detection and is particularly suitable for solving object ID switch caused by occlusion, entering and leaving of objects.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Editorial Material
Optics
Luciano A. Masullo, Fernando D. Stefani
Summary: Sequential excitation technique with minimal light enables precise localization of single fluorescent molecules with molecular resolution. Expanding this concept to multi-photon regimes can lead to even higher localization precision and deeper imaging in biological specimens.
LIGHT-SCIENCE & APPLICATIONS
(2022)
Article
Management
F. Clautiaux, B. Detienne, G. Guillot
Summary: This paper addresses the temporal knapsack problem and proposes a successive sublimation dynamic programming method to solve it. However, direct application of this method is not effective and further improvements are needed to compete with the best results in the literature.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2021)
Article
Biochemical Research Methods
Martin Maska, Vladimir Ulman, David Svoboda, Pavel Matula, Petr Matula, Cristina Ederra, Ainhoa Urbiola, Tomas Espana, Subramanian Venkatesan, Deepak M. W. Balak, Pavel Karas, Tereza Bolckova, Marketa Streitova, Craig Carthel, Stefano Coraluppi, Nathalie Harder, Karl Rohr, Klas E. G. Magnusson, Joakim Jalden, Helen M. Blau, Oleh Dzyubachyk, Pavel Krizek, Guy M. Hagen, David Pastor-Escuredo, Daniel Jimenez-Carretero, Maria J. Ledesma-Carbayo, Arrate Munoz-Barrutia, Erik Meijering, Michal Kozubek, Carlos Ortiz-de-Solorzano
Article
Biophysics
Maria A. Kiskowski, John F. Hancock, Anne K. Kenworthy
BIOPHYSICAL JOURNAL
(2009)
Meeting Abstract
Biophysics
Ramraj Velmurugan, Sripad Ram, Anish V. Abraham, Jerry Chao, Andrea Grosso, E. Sally Ward, Raimund J. Ober
BIOPHYSICAL JOURNAL
(2012)
Article
Biochemistry & Molecular Biology
Steffen J. Sahl, W. E. Moerner
CURRENT OPINION IN STRUCTURAL BIOLOGY
(2013)
Article
Biochemical Research Methods
Nicolas Chenouard, Ihor Smal, Fabrice de Chaumont, Martin Maska, Ivo F. Sbalzarini, Yuanhao Gong, Janick Cardinale, Craig Carthel, Stefano Coraluppi, Mark Winter, Andrew R. Cohen, William J. Godinez, Karl Rohr, Yannis Kalaidzidis, Liang Liang, James Duncan, Hongying Shen, Yingke Xu, Klas E. G. Magnusson, Joakim Jalden, Helen M. Blau, Perrine Paul-Gilloteaux, Philippe Roudot, Charles Kervrann, Francois Waharte, Jean-Yves Tinevez, Spencer L. Shorte, Joost Willemse, Katherine Celler, Gilles P. van Wezel, Han-Wei Dan, Yuh-Show Tsai, Carlos Ortiz de Solorzano, Jean-Christophe Olivo-Marin, Erik Meijering
Review
Microbiology
Andreas Gahlmann, W. E. Moerner
NATURE REVIEWS MICROBIOLOGY
(2014)
Article
Optics
Jerry Chao, Sripad Ram, Anish V. Abraham, E. Sally Ward, Raimund J. Ober
OPTICS COMMUNICATIONS
(2009)
Article
Optics
Anish V. Abraham, Sripad Ram, Jerry Chao, E. S. Ward, Raimund J. Ober
Article
Optics
Amir Tahmasbi, Sripad Ram, Jerry Chao, Anish V. Abraham, Felix W. Tang, E. Sally Ward, Raimund J. Ober
Proceedings Paper
Biochemical Research Methods
Thibault Lagache, Giacomo Nardi, Laetitia Bertot, Alexandre Grassart, Nathalie Sauvonnet, Jean-Christophe Olivo-Marin
2017 IEEE 14TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI 2017)
(2017)
Proceedings Paper
Microscopy
Amir Tahmasbi, Sripad Ram, Jerry Chao, Anish V. Abraham, E. Sally Ward, Raimund J. Ober
THREE-DIMENSIONAL AND MULTIDIMENSIONAL MICROSCOPY: IMAGE ACQUISITION AND PROCESSING XXII
(2015)
Meeting Abstract
Biophysics
Stephen M. Anthony, Anish V. Abraham, Jerry Chao, Sripad Ram, E. Sally Ward, Raimund J. Ober
BIOPHYSICAL JOURNAL
(2011)
Meeting Abstract
Biophysics
Anish V. Abraham, Sripad Ram, Jerry Chao, E. S. Ward, Raimund J. Ober
BIOPHYSICAL JOURNAL
(2009)
Meeting Abstract
Biophysics
Anish V. Abraham, Sripad Ram, Jerry Chao, E. Sally Ward, Raimund J. Ober
BIOPHYSICAL JOURNAL
(2009)
Article
Computer Science, Artificial Intelligence
Hong Liu, Dong Wei, Donghuan Lu, Xiaoying Tang, Liansheng Wang, Yefeng Zheng
Summary: This study proposes a framework based on hybrid 2D-3D convolutional neural networks for obtaining continuous 3D retinal layer surfaces from OCT volumes. The framework works well with both full and sparse annotations and utilizes alignment displacement vectors and layer segmentation to align the B-scans and segment the layers. Experimental results show that the framework outperforms state-of-the-art 2D deep learning methods in terms of layer segmentation accuracy and cross-B-scan 3D continuity.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Simon Oxenford, Ana Sofia Rios, Barbara Hollunder, Clemens Neudorfer, Alexandre Boutet, Gavin J. B. Elias, Jurgen Germann, Aaron Loh, Wissam Deeb, Bryan Salvato, Leonardo Almeida, Kelly D. Foote, Robert Amaral, Paul B. Rosenberg, David F. Tang-Wai, David A. Wolk, Anna D. Burke, Marwan N. Sabbagh, Stephen Salloway, M. Mallar Chakravarty, Gwenn S. Smith, Constantine G. Lyketsos, Michael S. Okun, William S., Zoltan Mari, Francisco A. Ponce, Andres Lozano, Wolf-Julian Neumann, Bassam Al-Fatly, Andreas Horn
Summary: Spatial normalization is a method to map subject brain images to an average template brain, allowing comparison of brain imaging results. We introduce a novel tool called WarpDrive, which enables manual refinements of image alignment after automated registration. The tool improves accuracy of data representation and aids in understanding patient outcomes.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Ricards Marcinkevics, Patricia Reis Wolfertstetter, Ugne Klimiene, Kieran Chin-Cheong, Alyssia Paschke, Julia Zerres, Markus Denzinger, David Niederberger, Sven Wellmann, Ece Ozkan, Christian Knorr, Julia E. Vogt
Summary: This study presents interpretable machine learning models for predicting the diagnosis, management, and severity of suspected appendicitis using ultrasound images. The proposed models utilize concept bottleneck models (CBM) that facilitate interpretation and intervention by clinicians, without compromising performance or requiring time-consuming image annotation.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Jian-Qing Zheng, Ziyang Wang, Baoru Huang, Ngee Han Lim, Bartlomiej W. Papiez
Summary: This article introduces a new method for medical image registration, which utilizes a separable motion backbone and a residual aligner module to better handle the discontinuous motion of multiple neighboring objects. The proposed method achieves excellent registration results on abdominal CT scans and lung CT scans.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Xiangqiong Wu, Guanghua Tan, Hongxia Luo, Zhilun Chen, Bin Pu, Shengli Li, Kenli Li
Summary: This study develops a user-friendly framework for the automated diagnosis of thyroid nodules in ultrasound videos, simulating the diagnostic workflow of radiologists. By interpreting image characteristics and modeling temporal contextual information, the efficiency and generalizability of the diagnosis can be improved.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Riddhish Bhalodia, Shireen Elhabian, Jadie Adams, Wenzheng Tao, Ladislav Kavan, Ross Whitaker
Summary: This paper introduces DeepSSM, a deep learning-based framework for image-to-shape modeling. By learning the functional mapping from images to low-dimensional shape descriptors, DeepSSM can directly infer statistical representation of anatomy from 3D images. Compared to traditional methods, DeepSSM eliminates the need for heavy manual preprocessing and segmentation, and significantly improves computational time.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Florentin Liebmann, Marco von Atzigen, Dominik Stutz, Julian Wolf, Lukas Zingg, Daniel Suter, Nicola A. Cavalcanti, Laura Leoty, Hooman Esfandiari, Jess G. Snedeker, Martin R. Oswald, Marc Pollefeys, Mazda Farshad, Philipp Furnstahl
Summary: This study presents a marker-less approach for automatic registration and real-time navigation of lumbar spinal fusion surgery using a deep neural network, avoiding radiation exposure and surgical errors. The method was validated on an ex-vivo surgery and a public dataset.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Piyush Tiwary, Kinjawl Bhattacharyya, A. P. Prathosh
Summary: Domain shift refers to the change of distributional characteristics between training and testing datasets, leading to performance drop. For medical image tasks, domain shift can be caused by changes in imaging modalities, devices, and staining mechanisms. Existing approaches based on generative models suffer from training difficulties and lack of diversity. In this paper, the authors propose the use of energy-based models (EBMs) for unpaired image-to-image translation in medical images. The proposed method, called Cycle Consistent Twin EBMs (CCT-EBM), employs a pair of EBMs in the latent space of an Auto-Encoder to ensure translation symmetry and coupling between domains.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Yutong Xie, Jianpeng Zhang, Lingqiao Liu, Hu Wang, Yiwen Ye, Johan Verjans, Yong Xia
Summary: This paper proposes a hybrid pre-training paradigm that combines self-supervised learning and supervised learning to improve the representation quality for medical image segmentation tasks. It introduces a reference task in self-supervised learning and optimizes the model using a gradient matching method. The experimental results demonstrate the effectiveness of this approach on multiple medical image segmentation benchmarks.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Youyi Song, Jing Zou, Kup-Sze Choi, Baiying Lei, Jing Qin
Summary: Cell classification is crucial for intelligent cervical cancer screening, but the variation in cells' appearance and shape poses challenges. A new learning algorithm, worse-case boosting, is proposed to improve classification accuracy for under-represented data. Experimental results demonstrate the effectiveness of this algorithm in two publicly available datasets, achieving a 4% improvement in accuracy.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Sangjoon Park, Eun Sun Lee, Kyung Sook Shin, Jeong Eun Lee, Jong Chul Ye
Summary: The increasing demand for AI systems to monitor human errors and abnormalities in healthcare presents challenges. This study presents a model called Medical X-VL, which is tailored for the medical domain and outperformed current state-of-the-art models in two medical image datasets. The model enables various zero-shot tasks for monitoring AI in the medical domain.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Anna Klimovskaia Susmelj, Berkan Lafci, Firat Ozdemir, Neda Davoudi, Xose Luis Dean-Ben, Fernando Perez-Cruz, Daniel Razansky
Summary: Optoacoustic imaging is a technique that uses optical excitation and ultrasound detection for biological tissue imaging. The quality of the images depends on the extent of tomographic coverage provided by the ultrasound detector arrays. However, full coverage is not always possible due to experimental constraints. The proposed signal domain adaptation network aims to reduce limited-view artifacts in the images.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Srijay Deshpande, Muhammad Dawood, Fayyaz Minhas, Nasir Rajpoot
Summary: In this work, a novel framework called SynCLay is proposed for automated synthesis of histology images based on user-defined cellular layouts. The framework can generate realistic and high-quality histology images with different cellular arrangements, which is helpful for studying the role of cells in the tumor microenvironment. The framework integrates a nuclear segmentation and classification model to refine nuclear structures and generate nuclear masks. Evaluation using quantitative metrics and feedback from pathologists shows that the synthetic images generated by SynCLay have high realism scores and can accurately differentiate between benign and malignant tumors.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Ahmed H. Shahin, An Zhao, Alexander C. Whitehead, Daniel C. Alexander, Joseph Jacob, David Barber
Summary: Survival analysis is a valuable tool in healthcare for predicting the time to specific events. This paper introduces CenTime, a novel approach that directly estimates the time to event. The method performs well with censored data and can be easily integrated with deep learning models. Compared to standard methods, CenTime offers superior performance in predicting event time while maintaining comparable ranking performance.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Bingyuan Liu, Jose Dolz, Adrian Galdran, Riadh Kobbi, Ismail Ben Ayed
Summary: Most segmentation losses, such as CE and Dice, are variants of the Cross-Entropy or Dice losses. This work provides a theoretical analysis that shows a deeper connection between CE and Dice than previously thought. From a constrained-optimization perspective, both CE and Dice decompose into similar ground-truth matching terms and region-size penalty terms. The analysis uncovers hidden region-size biases: Dice has an intrinsic bias towards extremely imbalanced solutions, while CE implicitly encourages the ground-truth region proportions. Based on this analysis, a principled and simple solution is proposed to explicitly control the region-size bias.
MEDICAL IMAGE ANALYSIS
(2024)