keyword
https://read.qxmd.com/read/38536700/dpnet-dual-path-network-for-real-time-object-detection-with-lightweight-attention
#1
JOURNAL ARTICLE
Quan Zhou, Huimin Shi, Weikang Xiang, Bin Kang, Longin Jan Latecki
The recent advances in compressing high-accuracy convolutional neural networks (CNNs) have witnessed remarkable progress in real-time object detection. To accelerate detection speed, lightweight detectors always have few convolution layers using a single-path backbone. Single-path architecture, however, involves continuous pooling and downsampling operations, always resulting in coarse and inaccurate feature maps that are disadvantageous to locate objects. On the other hand, due to limited network capacity, recent lightweight networks are often weak in representing large-scale visual data...
March 27, 2024: IEEE Transactions on Neural Networks and Learning Systems
https://read.qxmd.com/read/38536687/benchmarking-supervised-and-self-supervised-learning-methods-in-a-large-ultrasound-muti-task-images-dataset
#2
JOURNAL ARTICLE
Peizhong Liu, Jiansong Zhang, Xiuming Wu, Shunlan Liu, Yanli Wang, Longxiang Feng, Yong Diao, Zhonghua Liu, Guorong Lyu, Yongjian Chen
Deep learning in ultrasound(US) imaging aims to construct foundational models that accurately reflect the modality's unique characteristics. Nevertheless, the limited datasets and narrow task types have restricted this field in recent years. To address these challenges, we introduce US-MTD120K, a multi-task ultrasound dataset with 120,354 real-world two-dimensional images. This dataset covers three standard plane recognition and two diagnostic tasks in ultrasound imaging, providing a rich basis for model training and evaluation...
March 27, 2024: IEEE Journal of Biomedical and Health Informatics
https://read.qxmd.com/read/38536559/radiomics-based-machine-learning-in-the-differentiation-of-benign-and-malignant-bowel-wall-thickening-radiomics-in-bowel-wall-thickening
#3
JOURNAL ARTICLE
Hande Melike Bülbül, Gülen Burakgazi, Uğur Kesimal, Esat Kaba
PURPOSE: To distinguish malignant and benign bowel wall thickening (BWT) by using computed tomography (CT) texture features based on machine learning (ML) models and to compare its success with the clinical model and combined model. METHODS: One hundred twenty-two patients with BWT identified on contrast-enhanced abdominal CT and underwent colonoscopy were included in this retrospective study. Texture features were extracted from CT images using LifeX software. Feature selection and reduction were performed using the Least Absolute Shrinkage and Selection Operator (LASSO)...
March 27, 2024: Japanese Journal of Radiology
https://read.qxmd.com/read/38534892/mastering-sedation-and-associated-respiratory-events-through-simulation-based-training-a-randomised-controlled-trial-involving-non-anaesthesiology-residents
#4
JOURNAL ARTICLE
Jean-Noël Evain, Tran Do, Hakim Harkouk, Pierre Drolet, Roger Perron, Mihai Georgescu, Arnaud Robitaille, Issam Tanoubi
Non-anaesthetists commonly administer procedural sedation worldwide, posing the risk of respiratory events that can lead to severe complications. This study aimed to evaluate whether simulation-based learning could lead to enhancements in the clinical proficiency of non-anaesthesiology residents in managing sedation and related respiratory complications. Following the evaluation of baseline clinical performance through a pre-test simulation, 34 residents were randomly allocated to either participate in an innovative simulation-based learning module (intervention group) or view a brief self-learning video (control group)...
February 23, 2024: European journal of investigation in health, psychology and education
https://read.qxmd.com/read/38534843/bio-inspired-dark-adaptive-nighttime-object-detection
#5
JOURNAL ARTICLE
Kuo-Feng Hung, Kang-Ping Lin
Nighttime object detection is challenging due to dim, uneven lighting. The IIHS research conducted in 2022 shows that pedestrian anti-collision systems are less effective at night. Common solutions utilize costly sensors, such as thermal imaging and LiDAR, aiming for highly accurate detection. Conversely, this study employs a low-cost 2D image approach to address the problem by drawing inspiration from biological dark adaptation mechanisms, simulating functions like pupils and photoreceptor cells. Instead of relying on extensive machine learning with day-to-night image conversions, it focuses on image fusion and gamma correction to train deep neural networks for dark adaptation...
March 3, 2024: Biomimetics
https://read.qxmd.com/read/38534823/research-on-microgrid-optimal-dispatching-based-on-a-multi-strategy-optimization-of-slime-mould-algorithm
#6
JOURNAL ARTICLE
Yi Zhang, Yangkun Zhou
In order to cope with the problems of energy shortage and environmental pollution, carbon emissions need to be reduced and so the structure of the power grid is constantly being optimized. Traditional centralized power networks are not as capable of controlling and distributing non-renewable energy as distributed power grids. Therefore, the optimal dispatch of microgrids faces increasing challenges. This paper proposes a multi-strategy fusion slime mould algorithm (MFSMA) to tackle the microgrid optimal dispatching problem...
February 23, 2024: Biomimetics
https://read.qxmd.com/read/38534815/dendritic-growth-optimization-a-novel-nature-inspired-algorithm-for-real-world-optimization-problems
#7
JOURNAL ARTICLE
Ishaani Priyadarshini
In numerous scientific disciplines and practical applications, addressing optimization challenges is a common imperative. Nature-inspired optimization algorithms represent a highly valuable and pragmatic approach to tackling these complexities. This paper introduces Dendritic Growth Optimization (DGO), a novel algorithm inspired by natural branching patterns. DGO offers a novel solution for intricate optimization problems and demonstrates its efficiency in exploring diverse solution spaces. The algorithm has been extensively tested with a suite of machine learning algorithms, deep learning algorithms, and metaheuristic algorithms, and the results, both before and after optimization, unequivocally support the proposed algorithm's feasibility, effectiveness, and generalizability...
February 21, 2024: Biomimetics
https://read.qxmd.com/read/38534225/an-accelerometer-based-wearable-patch-for-robust-respiratory-rate-and-wheeze-detection-using-deep-learning
#8
JOURNAL ARTICLE
Brian Sang, Haoran Wen, Gregory Junek, Wendy Neveu, Lorenzo Di Francesco, Farrokh Ayazi
Wheezing is a critical indicator of various respiratory conditions, including asthma and chronic obstructive pulmonary disease (COPD). Current diagnosis relies on subjective lung auscultation by physicians. Enabling this capability via a low-profile, objective wearable device for remote patient monitoring (RPM) could offer pre-emptive, accurate respiratory data to patients. With this goal as our aim, we used a low-profile accelerometer-based wearable system that utilizes deep learning to objectively detect wheezing along with respiration rate using a single sensor...
February 22, 2024: Biosensors
https://read.qxmd.com/read/38532663/guided-inquiry-based-learning-to-enhance-student-engagement-confidence-and-learning
#9
JOURNAL ARTICLE
Vuvi H Nguyen, Richard Halpin, Anita R Joy-Thomas
PURPOSE: This study explores the impact of guided inquiry-based learning (IBL) on student engagement and performance in a dental hygiene program. The research was conducted with 36 dental hygiene students, focusing on enhancing student engagement using a guided IBL methodology that could ultimately lead to improved student performance and confidence. METHODS: Delivered through two interventions, pre-, post-, and delayed post-tests evaluated student performance and confidence, while the ASPECT survey assessed student engagement...
March 27, 2024: Journal of Dental Education
https://read.qxmd.com/read/38532481/early-detection-of-dark-affected-plant-mechanical-responses-using-enhanced-electrical-signals
#10
JOURNAL ARTICLE
Hongping Li, Nikou Fotouhi, Fan Liu, Hongchao Ji, Qian Wu
BACKGROUND: Mechanical damage to plants triggers local and systemic electrical signals that are eventually decoded into plant defense responses. These responses are constantly affected by other environmental stimuli in nature, for instance, light fluctuation. In recent years, studies on decoding plant electrical signals powered by various machine learning models are increasing in a sense of early prediction or detection of different environmental stresses that threaten plant growth or crop yields...
March 26, 2024: Plant Methods
https://read.qxmd.com/read/38532412/incorporating-a-situational-judgement-test-in-residency-selections-clinical-educational-and-organizational-outcomes
#11
JOURNAL ARTICLE
Anurag Saxena, Loni Desanghere, Kelly Dore, Harold Reiter
BACKGROUND: Computer-based assessment for sampling personal characteristics (Casper), an online situational judgement test, is a broad measure of personal and professional qualities. We examined the impact of Casper in the residency selection process on professionalism concerns, learning interventions and resource utilization at an institution. METHODS: In 2022, admissions data and information in the files of residents in difficulty (over three years pre- and post- Casper implementation) was used to determine the number of residents in difficulty, CanMEDS roles requiring a learning intervention, types of learning interventions (informal learning plans vs...
March 26, 2024: BMC Medical Education
https://read.qxmd.com/read/38531913/conceptual-framework-for-tinnitus-a-cognitive-model-in-practice
#12
JOURNAL ARTICLE
Iman Ghodratitoostani, Zahra Vaziri, Milton Miranda Neto, Camila de Giacomo Carneiro Barros, Alexandre Cláudio Botazzo Delbem, Miguel Angelo Hyppolito, Hamid Jalilvand, Francisco Louzada, Joao Pereira Leite
Tinnitus is a conscious attended awareness perception of sourceless sound. Widespread theoretical and evidence-based neurofunctional and psychological models have tried to explain tinnitus-related distress considering the influence of psychological and cognitive factors. However, tinnitus models seem to be less focused on causality, thereby easily misleading interpretations. Also, they may be incapable of individualization. This study proposes a Conceptual Cognitive Framework (CCF) providing insight into cognitive mechanisms involved in the predisposition, precipitation, and perpetuation of tinnitus and consequent cognitive-emotional disturbances...
March 26, 2024: Scientific Reports
https://read.qxmd.com/read/38531680/multimodal-learning-for-temporal-relation-extraction-in-clinical-texts
#13
JOURNAL ARTICLE
Timotej Knez, Slavko Žitnik
OBJECTIVES: This study focuses on refining temporal relation extraction within medical documents by introducing an innovative bimodal architecture. The overarching goal is to enhance our understanding of narrative processes in the medical domain, particularly through the analysis of extensive reports and notes concerning patient experiences. MATERIALS AND METHODS: Our approach involves the development of a bimodal architecture that seamlessly integrates information from both text documents and knowledge graphs...
March 26, 2024: Journal of the American Medical Informatics Association: JAMIA
https://read.qxmd.com/read/38531253/systematic-evaluation-of-machine-learning-enhanced-trifocal-iol-power-selection-for-axial-myopia-cataract-patients
#14
JOURNAL ARTICLE
Danmin Cao, Min Hu, Danlin Zhi, Jianheng Liang, Qian Tan, Qiong Lei, Maoyan Li, Hao Cheng, Li Wang, Weiwei Dai
PURPOSE: This study aimed to evaluate and optimize intraocular lens (IOL) power selection for cataract patients with high axial myopia receiving trifocal IOLs. DESIGN: A multi-center, retrospective observational case series was conducted. Patients having an axial length ≥26 mm and undergoing cataract surgery with trifocal IOL implanted were studied. METHODS: Preoperative biometric and postoperative outcome data from 139 eyes were collected to train and test various machine learning (ML) models (support vector machine, linear regression, and stacking regressor) using five-fold cross-validation...
March 14, 2024: Computers in Biology and Medicine
https://read.qxmd.com/read/38531192/empirical-tests-of-the-effectiveness-of-enchroma-multi-notch-filters-for-enhancing-color-vision-in-deuteranomaly
#15
JOURNAL ARTICLE
Lucy P Somers, Anna Franklin, Jenny M Bosten
Manufacturers of notch filter-based aids for color vision claim that their products can enhance color perception for people with anomalous trichromacy, a form of color vision deficiency (CVD). Anecdotal reports imply that people with CVD can have radically enhanced color vision when using the filters. However, existing empirical research largely focussed on the effect of notch filters on performance on diagnostic tests for CVD has not found that they have any substantial effect. Informed by a model of anomalous trichromatic color vision, we selected stimuli predicted to reveal the effects of EnChroma filters...
March 25, 2024: Vision Research
https://read.qxmd.com/read/38530499/trained-quantity-discrimination-in-invasive-red-eared-slider-and-a-comparison-with-the-native-stripe-necked-turtle
#16
JOURNAL ARTICLE
Feng-Chun Lin, Pei-Jen Lee Shaner, Ming-Ying Hsieh, Martin J Whiting, Si-Min Lin
Little is known about the behavioral and cognitive traits that best predict invasion success. Evidence is mounting that cognitive performance correlates with survival and fecundity, two pivotal factors for the successful establishment of invasive populations. We assessed the quantity discrimination ability of the globally invasive red-eared slider (Trachemys scripta elegans). We further compared it to that of the native stripe-necked turtle (Mauremys sinensis), which has been previously evaluated for its superior quantity discrimination ability...
March 26, 2024: Animal Cognition
https://read.qxmd.com/read/38530021/enhancing-predictions-with-a-stacking-ensemble-model-for-icu-mortality-risk-in-patients-with-sepsis-associated-encephalopathy
#17
JOURNAL ARTICLE
Xuhui Liu, Hao Niu, Jiahua Peng
OBJECTIVE: We identified predictive factors and developed a novel machine learning (ML) model for predicting mortality risk in patients with sepsis-associated encephalopathy (SAE). METHODS: In this retrospective cohort study, data from the Medical Information Mart for Intensive Care IV (MIMIC-IV) and eICU Collaborative Research Database were used for model development and external validation. The primary outcome was the in-hospital mortality rate among patients with SAE; the observed in-hospital mortality rate was 14...
March 2024: Journal of International Medical Research
https://read.qxmd.com/read/38529877/from-nmr-to-ai-designing-a-novel-chemical-representation-to-enhance-machine-learning-predictions-of-physicochemical-properties
#18
JOURNAL ARTICLE
Arkadiusz Leniak, Wojciech Pietruś, Rafał Kurczab
A novel approach to the utilization of nuclear magnetic resonance (NMR) spectroscopy data in the prediction of logD through machine learning algorithms is shown. In the analysis, a data set of 754 chemical compounds, organized into 30 clusters, was evaluated using advanced machine learning models, such as Support Vector Regression (SVR), Gradient Boosting, and AdaBoost, and comprehensive validation and testing methods were employed, including 10-fold cross-validation, bootstrapping, and leave-one-out. The study revealed the superior performance of the Bucket Integration method for dimensionality reduction, consistently yielding the lowest root mean square error (RMSE) across all data sets and normalization schemes...
March 26, 2024: Journal of Chemical Information and Modeling
https://read.qxmd.com/read/38529376/the-application-of-artificial-intelligence-for-rapid-on-site-evaluation-during-flexible-bronchoscopy
#19
JOURNAL ARTICLE
Shuang Yan, Yongfei Li, Lei Pan, Hua Jiang, Li Gong, Faguang Jin
BACKGROUND: Rapid On-Site Evaluation (ROSE) during flexible bronchoscopy (FB) can improve the adequacy of biopsy specimens and diagnostic yield of lung cancer. However, the lack of cytopathologists has restricted the wide use of ROSE. OBJECTIVE: To develop a ROSE artificial intelligence (AI) system using deep learning techniques to differentiate malignant from benign lesions based on ROSE cytological images, and evaluate the clinical performance of the ROSE AI system...
2024: Frontiers in Oncology
https://read.qxmd.com/read/38528694/automated-detection-and-segmentation-of-bone-metastases-on-spine-mri-using-u-net-a-multicenter-study
#20
MULTICENTER STUDY
Dong Hyun Kim, Jiwoon Seo, Ji Hyun Lee, Eun-Tae Jeon, DongYoung Jeong, Hee Dong Chae, Eugene Lee, Ji Hee Kang, Yoon-Hee Choi, Hyo Jin Kim, Jee Won Chai
OBJECTIVE: To develop and evaluate a deep learning model for automated segmentation and detection of bone metastasis on spinal MRI. MATERIALS AND METHODS: We included whole spine MRI scans of adult patients with bone metastasis: 662 MRI series from 302 patients (63.5 ± 11.5 years; male:female, 151:151) from three study centers obtained between January 2015 and August 2021 for training and internal testing (random split into 536 and 126 series, respectively) and 49 MRI series from 20 patients (65...
April 2024: Korean Journal of Radiology: Official Journal of the Korean Radiological Society
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