Short Text Semantic Similarity Measurement Approach Based on Semantic Network by Naamah Hussien Hameed, Adel M. Alimi, Ahmed T. Sadiq

“…The network representation is a visual representation of knowledge objects, their qualities, and their relationships. …”
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Automated Quantification of Retinopathy of Prematurity Stage via Ultrawidefield OCT by Spencer S. Burt, BA, Aaron S. Coyner, PhD, Elizabeth V. Roti, BS, Yakub Bayhaqi, PhD, John Jackson, MD, Mani K. Woodward, MS, Shuibin Ni, PhD, Susan R. Ostmo, MS, Guangru Liang, BS, Yali Jia, PhD, David Huang, MD, Michael F. Chiang, MD, Benjamin K. Young, MD, Yifan Jian, PhD, John Peter Campbell, MD

“…Purpose: Retinopathy of prematurity (ROP) stage is defined by the visual appearance of the vascular-avascular border, which reflects a spectrum of pathologic neurovascular tissue (NVT). …”
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Leveraging a foundation model zoo for cell similarity search in oncological microscopy across devices by Gabriel Kalweit, Gabriel Kalweit, Anusha Klett, Paula Silvestrini, Paula Silvestrini, Jens Rahnfeld, Jens Rahnfeld, Mehdi Naouar, Mehdi Naouar, Yannick Vogt, Yannick Vogt, Diana Infante, Diana Infante, Rebecca Berger, Jesús Duque-Afonso, Tanja Nicole Hartmann, Marie Follo, Marie Follo, Elitsa Bodurova-Spassova, Elitsa Bodurova-Spassova, Michael Lübbert, Michael Lübbert, Roland Mertelsmann, Roland Mertelsmann, Roland Mertelsmann, Joschka Boedecker, Joschka Boedecker, Joschka Boedecker, Evelyn Ullrich, Evelyn Ullrich, Evelyn Ullrich, Maria Kalweit, Maria Kalweit

“…This especially holds true for recognizing specific cell types and states in response to treatments.ObjectiveWe aim to develop an unsupervised approach using general vision foundation models trained on diverse and extensive imaging datasets to extract rich visual features for cell-analysis across devices, including both stained and unstained live cells. …”
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Artificial intelligence in Anatomy: Potential uses and challenges by Lubna Faisal

“…However, all the faculty, and health care professionals were trained and educated to use artificial intelligence to maintain factual knowledge secondly more important how to use it in future also [8]. …”
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YOLOv8 framework for COVID-19 and pneumonia detection using synthetic image augmentation by Uddin A Hasib, Raihan Md Abu, Jing Yang, Uzair Aslam Bhatti, Chin Soon Ku, Lip Yee Por

“…YOLOv8 and InceptionV3 models, fine-tuned via transfer learning, are trained on the augmented dataset. Grad-CAM is used for model explainability, while large language models (LLMs) support visualization analysis to enhance interpretability. …”
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Real-time Ultrasound-guided Lumbar Puncture: A Comparison of Two Techniques Using Simulation by Kara Samsel, David Wasiak, Elaine Situ-LaCasse, Srikar Adhikari, Josie Acuña

“…After a didactic session, participants then performed an ultrasound-guided LP on a training manikin, during which we collected procedure data. …”
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Uveitis output in high-impact clinical ophthalmology journals: a bibliometric analysis by Baotram V. Nguyen, Priyanka Bhatnagar, Daniel C. Lee, Meghan K. Berkenstock

“…Articles were screened using uveitis MeSH terms. Data analysis was performed using STATA to assess the relationship between the proportions of uveitis-focused articles and uveitis-trained editors. …”
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Predicting Short-Term Risk of Cardiovascular Events in the Elderly Population: A Retrospective Study in Shanghai, China by Zhu W, Tan S, Zhou Z, Zhao M, Wang Y, Li Q, Zheng Y, Shi J

“…The risk scoring was visualized through a nomogram, and the model performance was assessed using calibration plots and receiver operating characteristic curves.Results: A total of 9,636 individuals aged ≥ 60 years were included. …”
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Scalable quality control on processing of large diffusion-weighted and structural magnetic resonance imaging datasets. by Michael E Kim, Chenyu Gao, Nancy R Newlin, Gaurav Rudravaram, Aravind R Krishnan, Karthik Ramadass, Praitayini Kanakaraj, Kurt G Schilling, Blake E Dewey, David A Bennett, Sid O'Bryant, Robert C Barber, Derek Archer, Timothy J Hohman, Shunxing Bao, Zhiyuan Li, Bennett A Landman, Nazirah Mohd Khairi, Alzheimer’s Disease Neuroimaging Initiative, HABS-HD Study Team

“…Our proposed method satisfies the following design criteria: 1.) a consistent way to perform and manage quality control across a team of researchers, 2.) quick visualization of preprocessed data that minimizes the effort and time spent on the QC process without compromising the condition/caliber of the QC, and 3.) a way to aggregate QC results across pipelines and datasets that can be easily shared. …”
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Refractive Errors and Concomitant Strabismus in Children and Adolescents: A Hospital Based Observational Study by Anupam Singh, Omna Chawla, Rupal Verma, Vartika Saxena, Ranjeeta Kumari, Nisheeta Patnaik, Barun Kumar, Devesh Kumawat

“…Non-cycloplegic and cycloplegic refraction was performed by a trained optometrist. Glasses were prescribed to achieve the best corrected visual acuity (BCVA). …”
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Development and validation of machine learning models for osteoporosis prediction in chronic kidney disease patients: Data from National Health and Nutrition Examination survey by Hui Li, Ya Zhang, Chong Zhang

“…The best models were externally validated and visualized for interpretability. Results Among 3796 CKD patients, osteoporosis prevalence was 12.54% (7.28% in males and 17.57% in females). …”
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Predicting CO2 adsorption in KOH-activated biochar using advanced machine learning techniques by Raouf Hassan, Alireza Baghban

“…We employed a comprehensive suite of machine learning methods, like convolutional neural networks, random forests, artificial neural networks, linear regression, ridge and lasso regressions, elastic net, support vector machines, decision trees, gradient boosting machines, k-nearest neighbors, light gradient boosting machines, extreme gradient boosting, CatBoost, and Gaussian process, to build predictive models. These models were trained and validated on a dataset of 329 data points, assessed through performance metrics and visualizations. …”
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LoRA Fusion: Enhancing Image Generation by Dooho Choi, Jeonghyeon Im, Yunsick Sung

“…One emerging approach constructs several LoRA modules, but more than three typically decrease the generation performance of pre-trained models. The mixture-of-experts model solves the performance issue, but LoRA modules are not combined using text prompts; hence, generating images by combining LoRA modules does not dynamically reflect the user’s desired requirements. …”
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Impact of using various x-ray dataset in detecting tuberculosis based on deep learning by Muhammad Irhamsyah, Qurrata A’yuni, Khairun Saddami, Nasaruddin Nasaruddin, Khairul Munadi, Fitri Arnia

“…The subject matter is that the characteristics of tuberculosis are difficult to study visually. Therefore, a computer-aided system based on deep learning can be applied to X-ray image recognition. …”
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Development of an Algorithm for Semantic Segmentation of Earth Remote Sensing Data to Determine Phytoplankton Populations by Yu. V. Belova, I. F. Razveeva, E. O. Rakhimbaeva

“…The following metric values were obtained: Precision = 0.89, Recall = 0.88, F1 = 0.87, Dice = 0.87, and IoU = 0.79. Graphical visualization of the results of CNN learning on the training and validation sets showed good quality of model learning. …”
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Automatic segmentation model and machine learning model grounded in ultrasound radiomics for distinguishing between low malignant risk and intermediate-high malignant risk of adnex... by Lu Liu, Wenjun Cai, Feibo Zheng, Hongyan Tian, Yanping Li, Ting Wang, Xiaonan Chen, Wenjing Zhu

“…The SHapley Additive exPlanations were used for model interpretability and visualization. Results The FCN ResNet101 demonstrated the highest segmentation performance for adnexal masses (Dice similarity coefficient: 89.1%). …”
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Assessment of prostate cancer aggressiveness through the combined analysis of prostate MRI and 2.5D deep learning models by Yalei Wang, Yuqing Xin, Baoqi Zhang, Fuqiang Pan, Xu Li, Manman Zhang, Yushan Yuan, Lei Zhang, Peiqi Ma, Bo Guan, Yang Zhang

“…To ensure consistency in feature extraction, intraclass correlation coefficient (ICC) analysis was performed on features extracted by radiologists, followed by feature normalization using the mean and standard deviation of the training set. …”
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Application of Quantitative Interpretability to Evaluate CNN-Based Models for Medical Image Classification by Nuan Cui, Yingjie Wu, Guojiang Xin, Jiaze Wu, Liqin Zhong, Hao Liang

“…All models achieved high classification performance on the test set (accuracy >0.92, precision >0.89, recall >0.91), but VGG19 and AlexNet performed poorly on external validation. …”
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Detection and Classification of <i>Agave angustifolia</i> Haw Using Deep Learning Models by Idarh Matadamas, Erik Zamora, Teodulfo Aquino-Bolaños

“…The identification of damage through non-invasive tools based on visual information is important for reducing economic losses. …”
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User Intent-Based Music Generation Model Combining Actor-Critic Approach With MusicVAE by Soyoung Jang, Jaeho Lee

“…The model performance was evaluated using Spotify Energy-Valence analysis, PCA-based latent space visualization, and listening tests (200 subjects), and found to be superior to existing models in both conditioned performance and musical naturalness.…”
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