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Flipover outperforms dropout in deep learning Vis. Comput. Ind. Biomed. Art Pub Date : 2024-02-22 Yuxuan Liang, Chuang Niu, Pingkun Yan, Ge Wang
Flipover, an enhanced dropout technique, is introduced to improve the robustness of artificial neural networks. In contrast to dropout, which involves randomly removing certain neurons and their connections, flipover randomly selects neurons and reverts their outputs using a negative multiplier during training. This approach offers stronger regularization than conventional dropout, refining model performance
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Correction: Multi-task approach based on combined CNN-transformer for efficient segmentation and classification of breast tumors in ultrasound images Vis. Comput. Ind. Biomed. Art Pub Date : 2024-02-09 Jaouad Tagnamas, Hiba Ramadan, Ali Yahyaouy, Hamid Tairi
Correction: Vis. Comput. Ind. Biomed. Art 7, 2 (2024) https://doi.org/10.1186/s42492-024-00155-w Following publication of the original article [1], the authors reported that the wrong version of abstract and keywords were mistakenly inserted to this article. The original Abstract and Keywords were: Accurate segmentation of breast ultrasound (BUS) images is crucial for early diagnosis and treatment
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Convolutional neural network based data interpretable framework for Alzheimer’s treatment planning Vis. Comput. Ind. Biomed. Art Pub Date : 2024-02-01 Sazia Parvin, Sonia Farhana Nimmy, Md Sarwar Kamal
Alzheimer’s disease (AD) is a neurological disorder that predominantly affects the brain. In the coming years, it is expected to spread rapidly, with limited progress in diagnostic techniques. Various machine learning (ML) and artificial intelligence (AI) algorithms have been employed to detect AD using single-modality data. However, recent developments in ML have enabled the application of these methods
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Multi-task approach based on combined CNN-transformer for efficient segmentation and classification of breast tumors in ultrasound images Vis. Comput. Ind. Biomed. Art Pub Date : 2024-01-26 Jaouad Tagnamas, Hiba Ramadan, Ali Yahyaouy, Hamid Tairi
Accurate segmentation of breast ultrasound (BUS) images is crucial for early diagnosis and treatment of breast cancer. Further, the task of segmenting lesions in BUS images continues to pose significant challenges due to the limitations of convolutional neural networks (CNNs) in capturing long-range dependencies and obtaining global context information. Existing methods relying solely on CNNs have
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CT-based radiomics: predicting early outcomes after percutaneous transluminal renal angioplasty in patients with severe atherosclerotic renal artery stenosis Vis. Comput. Ind. Biomed. Art Pub Date : 2024-01-12 Jia Fu, Mengjie Fang, Zhiyong Lin, Jianxing Qiu, Min Yang, Jie Tian, Di Dong, Yinghua Zou
This study aimed to comprehensively evaluate non-contrast computed tomography (CT)-based radiomics for predicting early outcomes in patients with severe atherosclerotic renal artery stenosis (ARAS) after percutaneous transluminal renal angioplasty (PTRA). A total of 52 patients were retrospectively recruited, and their clinical characteristics and pretreatment CT images were collected. During a median
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Adaptive feature extraction method for capsule endoscopy images Vis. Comput. Ind. Biomed. Art Pub Date : 2023-12-11 Dingchang Wu, Yinghui Wang, Haomiao Ma, Lingyu Ai, Jinlong Yang, Shaojie Zhang, Wei Li
The traditional feature-extraction method of oriented FAST and rotated BRIEF (ORB) detects image features based on a fixed threshold; however, ORB descriptors do not distinguish features well in capsule endoscopy images. Therefore, a new feature detector that uses a new method for setting thresholds, called the adaptive threshold FAST and FREAK in capsule endoscopy images (AFFCEI), is proposed. This
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Comprehensive integrated analysis of MR and DCE-MR radiomics models for prognostic prediction in nasopharyngeal carcinoma Vis. Comput. Ind. Biomed. Art Pub Date : 2023-12-01 Hailin Li, Weiyuan Huang, Siwen Wang, Priya S. Balasubramanian, Gang Wu, Mengjie Fang, Xuebin Xie, Jie Zhang, Di Dong, Jie Tian, Feng Chen
Although prognostic prediction of nasopharyngeal carcinoma (NPC) remains a pivotal research area, the role of dynamic contrast-enhanced magnetic resonance (DCE-MR) has been less explored. This study aimed to investigate the role of DCR-MR in predicting progression-free survival (PFS) in patients with NPC using magnetic resonance (MR)- and DCE-MR-based radiomic models. A total of 434 patients with two
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Local imperceptible adversarial attacks against human pose estimation networks Vis. Comput. Ind. Biomed. Art Pub Date : 2023-11-21 Fuchang Liu, Shen Zhang, Hao Wang, Caiping Yan, Yongwei Miao
Deep neural networks are vulnerable to attacks from adversarial inputs. Corresponding attack research on human pose estimation (HPE), particularly for body joint detection, has been largely unexplored. Transferring classification-based attack methods to body joint regression tasks is not straightforward. Another issue is that the attack effectiveness and imperceptibility contradict each other. To solve
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Reliable knowledge graph fact prediction via reinforcement learning Vis. Comput. Ind. Biomed. Art Pub Date : 2023-11-20 Fangfang Zhou, Jiapeng Mi, Beiwen Zhang, Jingcheng Shi, Ran Zhang, Xiaohui Chen, Ying Zhao, Jian Zhang
Knowledge graph (KG) fact prediction aims to complete a KG by determining the truthfulness of predicted triples. Reinforcement learning (RL)-based approaches have been widely used for fact prediction. However, the existing approaches largely suffer from unreliable calculations on rule confidences owing to a limited number of obtained reasoning paths, thereby resulting in unreliable decisions on prediction
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Application and prospects of AI-based radiomics in ultrasound diagnosis Vis. Comput. Ind. Biomed. Art Pub Date : 2023-10-13 Haoyan Zhang, Zheling Meng, Jinyu Ru, Yaqing Meng, Kun Wang
Artificial intelligence (AI)-based radiomics has attracted considerable research attention in the field of medical imaging, including ultrasound diagnosis. Ultrasound imaging has unique advantages such as high temporal resolution, low cost, and no radiation exposure. This renders it a preferred imaging modality for several clinical scenarios. This review includes a detailed introduction to imaging
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Focus-RCNet: a lightweight recyclable waste classification algorithm based on focus and knowledge distillation Vis. Comput. Ind. Biomed. Art Pub Date : 2023-10-11 Dashun Zheng, Rongsheng Wang, Yaofei Duan, Patrick Cheong-Iao Pang, Tao Tan
Waste pollution is a significant environmental problem worldwide. With the continuous improvement in the living standards of the population and increasing richness of the consumption structure, the amount of domestic waste generated has increased dramatically, and there is an urgent need for further treatment. The rapid development of artificial intelligence has provided an effective solution for automated
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Novel 3D local feature descriptor of point clouds based on spatial voxel homogenization for feature matching Vis. Comput. Ind. Biomed. Art Pub Date : 2023-09-28 Jiong Yang, Jian Zhang, Zhengyang Cai, Dongyang Fang
Obtaining a 3D feature description with high descriptiveness and robustness under complicated nuisances is a significant and challenging task in 3D feature matching. This paper proposes a novel feature description consisting of a stable local reference frame (LRF) and a feature descriptor based on local spatial voxels. First, an improved LRF was designed by incorporating distance weights into Z- and
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Discrimination between leucine-rich glioma-inactivated 1 antibody encephalitis and gamma-aminobutyric acid B receptor antibody encephalitis based on ResNet18 Vis. Comput. Ind. Biomed. Art Pub Date : 2023-08-18 Jian Pan, Ruijuan Lv, Qun Wang, Xiaobin Zhao, Jiangang Liu, Lin Ai
This study aims to discriminate between leucine-rich glioma-inactivated 1 (LGI1) antibody encephalitis and gamma-aminobutyric acid B (GABAB) receptor antibody encephalitis using a convolutional neural network (CNN) model. A total of 81 patients were recruited for this study. ResNet18, VGG16, and ResNet50 were trained and tested separately using 3828 positron emission tomography image slices that contained
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Hyperparameter optimization for cardiovascular disease data-driven prognostic system Vis. Comput. Ind. Biomed. Art Pub Date : 2023-08-01 Jayson Saputra, Cindy Lawrencya, Jecky Mitra Saini, Suharjito Suharjito
Prediction and diagnosis of cardiovascular diseases (CVDs) based, among other things, on medical examinations and patient symptoms are the biggest challenges in medicine. About 17.9 million people die from CVDs annually, accounting for 31% of all deaths worldwide. With a timely prognosis and thorough consideration of the patient’s medical history and lifestyle, it is possible to predict CVDs and take
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Survey of methods and principles in three-dimensional reconstruction from two-dimensional medical images Vis. Comput. Ind. Biomed. Art Pub Date : 2023-07-27 Mriganka Sarmah, Arambam Neelima, Heisnam Rohen Singh
Three-dimensional (3D) reconstruction of human organs has gained attention in recent years due to advances in the Internet and graphics processing units. In the coming years, most patient care will shift toward this new paradigm. However, development of fast and accurate 3D models from medical images or a set of medical scans remains a daunting task due to the number of pre-processing steps involved
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Vision transformer architecture and applications in digital health: a tutorial and survey Vis. Comput. Ind. Biomed. Art Pub Date : 2023-07-10 Khalid Al-hammuri, Fayez Gebali, Awos Kanan, Ilamparithi Thirumarai Chelvan
The vision transformer (ViT) is a state-of-the-art architecture for image recognition tasks that plays an important role in digital health applications. Medical images account for 90% of the data in digital medicine applications. This article discusses the core foundations of the ViT architecture and its digital health applications. These applications include image segmentation, classification, detection
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DB-DCAFN: dual-branch deformable cross-attention fusion network for bacterial segmentation Vis. Comput. Ind. Biomed. Art Pub Date : 2023-07-04 Jingkun Wang, Xinyu Ma, Long Cao, Yilin Leng, Zeyi Li, Zihan Cheng, Yuzhu Cao, Xiaoping Huang, Jian Zheng
Sputum smear tests are critical for the diagnosis of respiratory diseases. Automatic segmentation of bacteria from sputum smear images is important for improving diagnostic efficiency. However, this remains a challenging task owing to the high interclass similarity among different categories of bacteria and the low contrast of the bacterial edges. To explore more levels of global pattern features to
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Editorial: advances in deep learning techniques for biomedical imaging Vis. Comput. Ind. Biomed. Art Pub Date : 2023-06-21 Chuang Niu, Ge Wang
The field of biomedical imaging has been revolutionized by deep learning techniques. This special issue is focused on the theme of “AI-based Image Analysis”. Because there are so many conferences and journals in this field, our special issue can only be a small snapshot of a much bigger and highly dynamic picture. In this special issue, we present six papers that highlight the power of deep learning
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Beyond the horizon: immersive developments for animal ecology research Vis. Comput. Ind. Biomed. Art Pub Date : 2023-06-20 Ying Zhang, Karsten Klein, Falk Schreiber, Kamran Safi
More diverse data on animal ecology are now available. This “data deluge” presents challenges for both biologists and computer scientists; however, it also creates opportunities to improve analysis and answer more holistic research questions. We aim to increase awareness of the current opportunity for interdisciplinary research between animal ecology researchers and computer scientists. Immersive analytics (IA)
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Systematic review of digital twin technology and applications Vis. Comput. Ind. Biomed. Art Pub Date : 2023-05-30 Jun-Feng Yao, Yong Yang, Xue-Cheng Wang, Xiao-Peng Zhang
As one of the most important applications of digitalization, intelligence, and service, the digital twin (DT) breaks through the constraints of time, space, cost, and security on physical entities, expands and optimizes the relevant functions of physical entities, and enhances their application value. This phenomenon has been widely studied in academia and industry. In this study, the concept and definition
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Translating radiology reports into plain language using ChatGPT and GPT-4 with prompt learning: results, limitations, and potential Vis. Comput. Ind. Biomed. Art Pub Date : 2023-05-18 Qing Lyu, Josh Tan, Michael E. Zapadka, Janardhana Ponnatapura, Chuang Niu, Kyle J. Myers, Ge Wang, Christopher T. Whitlow
The large language model called ChatGPT has drawn extensively attention because of its human-like expression and reasoning abilities. In this study, we investigate the feasibility of using ChatGPT in experiments on translating radiology reports into plain language for patients and healthcare providers so that they are educated for improved healthcare. Radiology reports from 62 low-dose chest computed
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EM-Gaze: eye context correlation and metric learning for gaze estimation Vis. Comput. Ind. Biomed. Art Pub Date : 2023-05-05 Jinchao Zhou, Guoan Li, Feng Shi, Xiaoyan Guo, Pengfei Wan, Miao Wang
In recent years, deep learning techniques have been used to estimate gaze—a significant task in computer vision and human-computer interaction. Previous studies have made significant achievements in predicting 2D or 3D gazes from monocular face images. This study presents a deep neural network for 2D gaze estimation on mobile devices. It achieves state-of-the-art 2D gaze point regression error, while
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Simulation and optimization of scrap wagon dismantling system based on Plant Simulation Vis. Comput. Ind. Biomed. Art Pub Date : 2023-04-24 Hai-Qing Chen, Yu-De Dong, Fei Hu, Ming-Ming Liu, Shi-Bao Zhang
Based on the existing plant layout and process flow, a simulation analysis was conducted using the Plant Simulation platform with the utilization efficiency of each station and production capacity of the dismantling system as indicators. A problem with long-term suspension in the disassembly process was determined. Based on the two optimization directions of increasing material transportation equipment
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Defect detection of gear parts in virtual manufacturing Vis. Comput. Ind. Biomed. Art Pub Date : 2023-03-29 Zhenxing Xu, Aizeng Wang, Fei Hou, Gang Zhao
Gears play an important role in virtual manufacturing systems for digital twins; however, the image of gear tooth defects is difficult to acquire owing to its non-convex shape. In this study, a deep learning network is proposed to detect gear defects based on their point cloud representation. This approach mainly consists of three steps: (1) Various types of gear defects are classified into four cases
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Rendering algorithms for aberrated human vision simulation Vis. Comput. Ind. Biomed. Art Pub Date : 2023-03-17 István Csoba, Roland Kunkli
Vision-simulated imagery―the process of generating images that mimic the human visual system―is a valuable tool with a wide spectrum of possible applications, including visual acuity measurements, personalized planning of corrective lenses and surgeries, vision-correcting displays, vision-related hardware development, and extended reality discomfort reduction. A critical property of human vision is
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Robustness optimization for rapid prototyping of functional artifacts based on visualized computing digital twins Vis. Comput. Ind. Biomed. Art Pub Date : 2023-02-27 Jinghua Xu, Kunqian Liu, Linxuan Wang, Hongshuai Guo, Jiangtao Zhan, Xiaojian Liu, Shuyou Zhang, Jianrong Tan
This study presents a robustness optimization method for rapid prototyping (RP) of functional artifacts based on visualized computing digital twins (VCDT). A generalized multiobjective robustness optimization model for RP of scheme design prototype was first built, where thermal, structural, and multidisciplinary knowledge could be integrated for visualization. To implement visualized computing, the
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Preliminary landscape analysis of deep tomographic imaging patents Vis. Comput. Ind. Biomed. Art Pub Date : 2023-01-23 Qingsong Yang, Donna L. Lizotte, Wenxiang Cong, Ge Wang
Over recent years, the importance of the patent literature has become increasingly more recognized in the academic setting. In the context of artificial intelligence, deep learning, and data sciences, patents are relevant to not only industry but also academe and other communities. In this article, we focus on deep tomographic imaging and perform a preliminary landscape analysis of the related patent
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Photon-counting computed tomography thermometry via material decomposition and machine learning Vis. Comput. Ind. Biomed. Art Pub Date : 2023-01-14 Wang, Nathan, Li, Mengzhou, Haverinen, Petteri
Thermal ablation procedures, such as high intensity focused ultrasound and radiofrequency ablation, are often used to eliminate tumors by minimally invasively heating a focal region. For this task, real-time 3D temperature visualization is key to target the diseased tissues while minimizing damage to the surroundings. Current computed tomography (CT) thermometry is based on energy-integrated CT, tissue-specific
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An efficient non-iterative smoothed particle hydrodynamics fluid simulation method with variable smoothing length Vis. Comput. Ind. Biomed. Art Pub Date : 2023-01-03 Li, Min, Li, Hongshu, Meng, Weiliang, Zhu, Jian, Zhang, Gary
In classical smoothed particle hydrodynamics (SPH) fluid simulation approaches, the smoothing length of Lagrangian particles is typically constant. One major disadvantage is the lack of adaptiveness, which may compromise accuracy in fluid regions such as splashes and surfaces. Attempts to address this problem used variable smoothing lengths. Yet the existing methods are computationally complex and
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Survey on computational 3D visual optical art design Vis. Comput. Ind. Biomed. Art Pub Date : 2022-12-19 Wu, Kang, Fu, Xiao-Ming, Chen, Renjie, Liu, Ligang
Visual arts refer to art experienced primarily through vision. 3D visual optical art is one of them. Artists use their rich imagination and experience to combine light and objects to give viewers an unforgettable visual experience. However, the design process involves much trial and error; therefore, it is often very time-consuming. This has prompted many researchers to focus on proposing various algorithms
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Deep learning tomographic reconstruction through hierarchical decomposition of domain transforms Vis. Comput. Ind. Biomed. Art Pub Date : 2022-12-09 Fu, Lin, De Man, Bruno
Deep learning (DL) has shown unprecedented performance for many image analysis and image enhancement tasks. Yet, solving large-scale inverse problems like tomographic reconstruction remains challenging for DL. These problems involve non-local and space-variant integral transforms between the input and output domains, for which no efficient neural network models are readily available. A prior attempt
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Cardiac CT blooming artifacts: clinical significance, root causes and potential solutions Vis. Comput. Ind. Biomed. Art Pub Date : 2022-12-09 Pack, Jed D., Xu, Mufeng, Wang, Ge, Baskaran, Lohendran, Min, James, De Man, Bruno
This review paper aims to summarize cardiac CT blooming artifacts, how they present clinically and what their root causes and potential solutions are. A literature survey was performed covering any publications with a specific interest in calcium blooming and stent blooming in cardiac CT. The claims from literature are compared and interpreted, aiming at narrowing down the root causes and most promising
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Optimization design of two-stage amplification micro-drive system without additional motion based on particle swarm optimization algorithm Vis. Comput. Ind. Biomed. Art Pub Date : 2022-11-25 Yang, Manzhi, Wei, Kaiyang, Zhang, Chuanwei, Liu, Dandan, Yang, Yizhi, Han, Feiyan, Zhao, Shuanfeng
With the increasing requirements of precision mechanical systems in electronic packaging, ultra-precision machining, biomedicine and other high-tech fields, it is necessary to study a precision two-stage amplification micro-drive system that can safely provide high precision and a large amplification ratio. In view of the disadvantages of the current two-stage amplification and micro-drive system,
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Reinforcement learning method for machining deformation control based on meta-invariant feature space Vis. Comput. Ind. Biomed. Art Pub Date : 2022-11-24 Zhao, Yujie, Liu, Changqing, Zhao, Zhiwei, Tang, Kai, He, Dong
Precise control of machining deformation is crucial for improving the manufacturing quality of structural aerospace components. In the machining process, different batches of blanks have different residual stress distributions, which pose a significant challenge to machining deformation control. In this study, a reinforcement learning method for machining deformation control based on a meta-invariant
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Machine learning for enumeration of cell colony forming units Vis. Comput. Ind. Biomed. Art Pub Date : 2022-11-05 Zhang, Louis
As one of the most widely used assays in biological research, an enumeration of the bacterial cell colonies is an important but time-consuming and labor-intensive process. To speed up the colony counting, a machine learning method is presented for counting the colony forming units (CFUs), which is referred to as CFUCounter. This cell-counting program processes digital images and segments bacterial
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Two fully automated data-driven 3D whole-breast segmentation strategies in MRI for MR-based breast density using image registration and U-Net with a focus on reproducibility Vis. Comput. Ind. Biomed. Art Pub Date : 2022-10-11 Ying, Jia, Cattell, Renee, Zhao, Tianyun, Lei, Lan, Jiang, Zhao, Hussain, Shahid M., Gao, Yi, Chow, H.-H. Sherry, Stopeck, Alison T., Thompson, Patricia A., Huang, Chuan
Presence of higher breast density (BD) and persistence over time are risk factors for breast cancer. A quantitatively accurate and highly reproducible BD measure that relies on precise and reproducible whole-breast segmentation is desirable. In this study, we aimed to develop a highly reproducible and accurate whole-breast segmentation algorithm for the generation of reproducible BD measures. Three
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Recent developments of the reconstruction in magnetic particle imaging Vis. Comput. Ind. Biomed. Art Pub Date : 2022-10-01 Yin, Lin, Li, Wei, Du, Yang, Wang, Kun, Liu, Zhenyu, Hui, Hui, Tian, Jie
Magnetic particle imaging (MPI) is an emerging molecular imaging technique with high sensitivity and temporal-spatial resolution. Image reconstruction is an important research topic in MPI, which converts an induced voltage signal into the image of superparamagnetic iron oxide particles concentration distribution. MPI reconstruction primarily involves system matrix- and x-space-based methods. In this
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Superiority of quadratic over conventional neural networks for classification of gaussian mixture data Vis. Comput. Ind. Biomed. Art Pub Date : 2022-09-28 Qi, Tianrui, Wang, Ge
To enrich the diversity of artificial neurons, a type of quadratic neurons was proposed previously, where the inner product of inputs and weights is replaced by a quadratic operation. In this paper, we demonstrate the superiority of such quadratic neurons over conventional counterparts. For this purpose, we train such quadratic neural networks using an adapted backpropagation algorithm and perform
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Optical neuroimaging: advancing transcranial magnetic stimulation treatments of psychiatric disorders Vis. Comput. Ind. Biomed. Art Pub Date : 2022-09-08 Jiang, Shixie, Carpenter, Linda L., Jiang, Huabei
Transcranial magnetic stimulation (TMS) has been established as an important and effective treatment for various psychiatric disorders. However, its effectiveness has likely been limited due to the dearth of neuronavigational tools for targeting purposes, unclear ideal stimulation parameters, and a lack of knowledge regarding the physiological response of the brain to TMS in each psychiatric condition
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A framework from point clouds to workpieces Vis. Comput. Ind. Biomed. Art Pub Date : 2022-08-23 Shen, Li-Yong, Wang, Meng-Xing, Ma, Hong-Yu, Feng, Yi-Fei, Yuan, Chun-Ming
Combining computer-aided design and computer numerical control (CNC) with global technical connections have become interesting topics in the manufacturing industry. A framework was implemented that includes point clouds to workpieces and consists of a mesh generation from geometric data, optimal surface segmentation for CNC, and tool path planning with a certified scallop height. The latest methods
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Open-source algorithm and software for computed tomography-based virtual pancreatoscopy and other applications Vis. Comput. Ind. Biomed. Art Pub Date : 2022-08-03 Huang, Haofan, Yu, Xiaxia, Tian, Mu, He, Weizhen, Li, Shawn Xiang, Liang, Zhengrong, Gao, Yi
Pancreatoscopy plays a significant role in the diagnosis and treatment of pancreatic diseases. However, the risk of pancreatoscopy is remarkably greater than that of other endoscopic procedures, such as gastroscopy and bronchoscopy, owing to its severe invasiveness. In comparison, virtual pancreatoscopy (VP) has shown notable advantages. However, because of the low resolution of current computed tomography
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STTG-net: a Spatio-temporal network for human motion prediction based on transformer and graph convolution network Vis. Comput. Ind. Biomed. Art Pub Date : 2022-07-29 Chen, Lujing, Liu, Rui, Yang, Xin, Zhou, Dongsheng, Zhang, Qiang, Wei, Xiaopeng
In recent years, human motion prediction has become an active research topic in computer vision. However, owing to the complexity and stochastic nature of human motion, it remains a challenging problem. In previous works, human motion prediction has always been treated as a typical inter-sequence problem, and most works have aimed to capture the temporal dependence between successive frames. However
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Improved non-uniform subdivision scheme with modified Eigen-polyhedron Vis. Comput. Ind. Biomed. Art Pub Date : 2022-07-11 Zhang, Jingjing, Tian, Yufeng, Li, Xin
In this study, a systematic refinement method was developed for non-uniform Catmull-Clark subdivision surfaces to improve the quality of the surface at extraordinary points (EPs). The developed method modifies the eigenpolyhedron by designing the angles between two adjacent edges that contain an EP. Refinement rules are then formulated with the help of the modified eigenpolyhedron. Numerical experiments
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Curve intersection based on cubic hybrid clipping Vis. Comput. Ind. Biomed. Art Pub Date : 2022-06-22 Wu, Yaqiong, Li, Xin
This study presents a novel approach to computing all intersections between two Bézier curves using cubic hybrid clipping. Each intersection is represented by two strip intervals that contain an intersection. In each step, one curve is bounded by two fat lines, and the other is bounded by two cubic Bézier curves, clipping away the domain that does not contain the intersections. By selecting the moving
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Vector textures derived from higher order derivative domains for classification of colorectal polyps Vis. Comput. Ind. Biomed. Art Pub Date : 2022-06-14 Cao, Weiguo, Pomeroy, Marc J., Liang, Zhengrong, Abbasi, Almas F., Pickhardt, Perry J., Lu, Hongbing
Textures have become widely adopted as an essential tool for lesion detection and classification through analysis of the lesion heterogeneities. In this study, higher order derivative images are being employed to combat the challenge of the poor contrast across similar tissue types among certain imaging modalities. To make good use of the derivative information, a novel concept of vector texture is
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Collision-aware interactive simulation using graph neural networks Vis. Comput. Ind. Biomed. Art Pub Date : 2022-06-07 Zhu, Xin, Qian, Yinling, Wang, Qiong, Feng, Ziliang, Heng, Pheng-Ann
Deep simulations have gained widespread attention owing to their excellent acceleration performances. However, these methods cannot provide effective collision detection and response strategies. We propose a deep interactive physical simulation framework that can effectively address tool-object collisions. The framework can predict the dynamic information by considering the collision state. In particular
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Robust facial expression recognition system in higher poses Vis. Comput. Ind. Biomed. Art Pub Date : 2022-05-16 Owusu, Ebenezer, Appati, Justice Kwame, Okae, Percy
Facial expression recognition (FER) has numerous applications in computer security, neuroscience, psychology, and engineering. Owing to its non-intrusiveness, it is considered a useful technology for combating crime. However, FER is plagued with several challenges, the most serious of which is its poor prediction accuracy in severe head poses. The aim of this study, therefore, is to improve the recognition
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Analytical study of two feature extraction methods in comparison with deep learning methods for classification of small metal objects Vis. Comput. Ind. Biomed. Art Pub Date : 2022-05-10 Amraee, Somaieh, Chinipardaz, Maryam, Charoosaei, Mohammadali
This paper addresses the efficiency of two feature extraction methods for classifying small metal objects including screws, nuts, keys, and coins: the histogram of oriented gradients (HOG) and local binary pattern (LBP). The desired features for the labeled images are first extracted and saved in the form of a feature matrix. Using three different classification methods (non-parametric K-nearest neighbors
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Correction: DCAU-Net: dense convolutional attention U-Net for segmentation of intracranial aneurysm images Vis. Comput. Ind. Biomed. Art Pub Date : 2022-05-08 Yuan, Wenwen, Peng, Yanjun, Guo, Yanfei, Ren, Yande, Xue, Qianwen
Correction to: Vis Comput Ind Biomed Art 5, 9 (2022) https://doi.org/10.1186/s42492-022-00105-4 Following publication of the original article [1], the authors identified an error in Figs. 6 and 8 due to a typesetting error. The correct figures are given below. Fig. 6 Prediction maps for the models with different components, on the MICCAI 2020 ADAM testing set. The first column is the MRA image of an
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Influence of postural changes on haemodynamics in internal carotid artery bifurcation aneurysm using numerical methods Vis. Comput. Ind. Biomed. Art Pub Date : 2022-04-08 Ballambat, Raghuvir Pai, Zuber, Mohammad, Khader, Shah Mohammed Abdul, Ayachit, Anurag, Ahmad, Kamarul Arifin bin, Vedula, Rajanikanth Rao, Kamath, Sevagur Ganesh, Shuaib, Ibrahim Lutfi
Cerebral intracranial aneurysms are serious problems that can lead to stroke, coma, and even death. The effect of blood flow on cerebral aneurysms and their relationship with rupture are unknown. In addition, postural changes and their relevance to haemodynamics of blood flow are difficult to measure in vivo using clinical imaging alone. Computational simulations investigating the detailed haemodynamics
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Acquisition repeatability of MRI radiomics features in the head and neck: a dual-3D-sequence multi-scan study Vis. Comput. Ind. Biomed. Art Pub Date : 2022-04-01 Xue, Cindy, Yuan, Jing, Zhou, Yihang, Wong, Oi Lei, Cheung, Kin Yin, Yu, Siu Ki
Radiomics has increasingly been investigated as a potential biomarker in quantitative imaging to facilitate personalized diagnosis and treatment of head and neck cancer (HNC), a group of malignancies associated with high heterogeneity. However, the feature reliability of radiomics is a major obstacle to its broad validity and generality in application to the highly heterogeneous head and neck (HN)
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DCAU-Net: dense convolutional attention U-Net for segmentation of intracranial aneurysm images Vis. Comput. Ind. Biomed. Art Pub Date : 2022-03-28 Yuan, Wenwen, Peng, Yanjun, Guo, Yanfei, Ren, Yande, Xue, Qianwen
Segmentation of intracranial aneurysm images acquired using magnetic resonance angiography (MRA) is essential for medical auxiliary treatments, which can effectively prevent subarachnoid hemorrhages. This paper proposes an image segmentation model based on a dense convolutional attention U-Net, which fuses deep and rich semantic information with shallow-detail information for adaptive and accurate
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Preoperative prediction of lymph node metastasis using deep learning-based features Vis. Comput. Ind. Biomed. Art Pub Date : 2022-03-07 Cattell, Renee, Ying, Jia, Lei, Lan, Ding, Jie, Chen, Shenglan, Serrano Sosa, Mario, Huang, Chuan
Lymph node involvement increases the risk of breast cancer recurrence. An accurate non-invasive assessment of nodal involvement is valuable in cancer staging, surgical risk, and cost savings. Radiomics has been proposed to pre-operatively predict sentinel lymph node (SLN) status; however, radiomic models are known to be sensitive to acquisition parameters. The purpose of this study was to develop a
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Skin lesion classification system using a K-nearest neighbor algorithm Vis. Comput. Ind. Biomed. Art Pub Date : 2022-03-01 Hatem, Mustafa Qays
One of the most critical steps in medical health is the proper diagnosis of the disease. Dermatology is one of the most volatile and challenging fields in terms of diagnosis. Dermatologists often require further testing, review of the patient’s history, and other data to ensure a proper diagnosis. Therefore, finding a method that can guarantee a proper trusted diagnosis quickly is essential. Several
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Features of hardware implementation of quasi-continuous observation devices with discrete receivers Vis. Comput. Ind. Biomed. Art Pub Date : 2022-02-08 Maryliv, Oleksandr, Slonov, Mykhailo
This article proposes an approach to the formalization of tasks and conditions for the hardware implementation of quasi-continuous observation devices with discrete receivers in remote sensing systems. Observation devices with a matrix are used in medicine, ecology, aerospace photography, and geodesy, among other fields. In the discrete receivers, the sampling of an image in the matrix receiver into
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Non-rigid registration of medical images based on \( {S}_2^1\left({\Delta}_{mn}^{(2)}\right) \) non-tensor product B-spline Vis. Comput. Ind. Biomed. Art Pub Date : 2022-02-02 Zheng, Qi, Liu, Chaoyue, Chang, Jincai
In this study, a non-tensor product B-spline algorithm is applied to the search space of the registration process, and a new method of image non-rigid registration is proposed. The tensor product B-spline is a function defined in the two directions of x and y, while the non-tensor product B-spline $$ {S}_2^1\left({\Delta}_{mn}^{(2)}\right) $$ is defined in four directions on the 2-type triangulation
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Iterative analytic extension in tomographic imaging Vis. Comput. Ind. Biomed. Art Pub Date : 2022-02-01 Zeng, Gengsheng L.
If a spatial-domain function has a finite support, its Fourier transform is an entire function. The Taylor series expansion of an entire function converges at every finite point in the complex plane. The analytic continuation theory suggests that a finite-sized object can be uniquely determined by its frequency components in a very small neighborhood. Trying to obtain such an exact Taylor expansion
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Comparative analysis of proficiencies of various textures and geometric features in breast mass classification using k-nearest neighbor Vis. Comput. Ind. Biomed. Art Pub Date : 2022-01-12 Singh, Harmandeep, Sharma, Vipul, Singh, Damanpreet
This paper introduces a comparative analysis of the proficiencies of various textures and geometric features in the diagnosis of breast masses on mammograms. An improved machine learning-based framework was developed for this study. The proposed system was tested using 106 full field digital mammography images from the INbreast dataset, containing a total of 115 breast mass lesions. The proficiencies
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Spatially resolved transcriptomics in immersive environments Vis. Comput. Ind. Biomed. Art Pub Date : 2022-01-10 Bienroth, Denis, Nim, Hieu T., Garkov, Dimitar, Klein, Karsten, Jaeger-Honz, Sabrina, Ramialison, Mirana, Schreiber, Falk
Spatially resolved transcriptomics is an emerging class of high-throughput technologies that enable biologists to systematically investigate the expression of genes along with spatial information. Upon data acquisition, one major hurdle is the subsequent interpretation and visualization of the datasets acquired. To address this challenge, VR-Cardiomics is presented, which is a novel data visualization
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A projection-domain iterative algorithm for metal artifact reduction by minimizing the total-variation norm and the negative-pixel energy Vis. Comput. Ind. Biomed. Art Pub Date : 2022-01-02 Zeng, Gengsheng L.
Metal objects in X-ray computed tomography can cause severe artifacts. The state-of-the-art metal artifact reduction methods are in the sinogram inpainting category and are iterative methods. This paper proposes a projection-domain algorithm to reduce the metal artifacts. In this algorithm, the unknowns are the metal-affected projections, while the objective function is set up in the image domain.