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Simulated deep CT characterization of liver metastases with high-resolution filtered back projection reconstruction

Visual computing for Industry,Biomedicine,and Art 2024年第1期7卷 257-271页

作者： Christopher Wiedeman Peter Lorraine Ge Wang Richard Do Amber Simpson Jacob Peoples Bruno De Man Department of Electrical and Computer Engineering Rensselaer Polytechnic InstituteTroyNY 12180USA GE Research-Healthcare NiskayunaNY 12309USA Department of Biomedical Engineering Rensselaer Polytechnic InstituteTroyNY 12180USA Department of Radiology Memorial Sloan Kettering Cancer CenterNew YorkNY 10065USA Biomedical Computing and Informatics Queen’s UniversityKingstonON K7L 3N6Canada

Early diagnosis and accurate prognosis of colorectal cancer is critical for determining optimal treatment plans and maximizing patient outcomes,especially as the disease progresses into liver *** tomography(CT)is a frontline tool for this task;however,the preservation of predictive radiomic features is highly dependent on the scanning protocol and reconstruction *** hypothesized that image reconstruction with a highfrequency kernel could result in a better characterization of liver metastases features via deep neural *** kernel produces images that appear noisier but preserve more sinogram information.A simulation pipeline was developed to study the effects of imaging parameters on the ability to characterize the features of liver *** pipeline utilizes a fractal approach to generate a diverse population of shapes representing virtual metastases,and then it superimposes them on a realistic CT liver region to perform a virtual CT scan using *** of 10,000 liver metastases were generated,scanned,and reconstructed using either standard or high-frequency *** data were used to train and validate deep neural networks to recover crafted metastases characteristics,such as internal heterogeneity,edge sharpness,and edge fractal *** the absence of noise,models scored,on average,12.2%(α=0.012)and 7.5%(α=0.049)lower squared error for characterizing edge sharpness and fractal dimension,respectively,when using high-frequency reconstructions compared to ***,the differences in performance were statistically insignificant when a typical level of CT noise was simulated in the clinical *** results suggest that high-frequency reconstruction kernels can better preserve information for downstream artificial intelligence-based radiomic characterization,provided that noise is *** work should investigate the informationpreserving kernels in datasets with clinical labels.

关键词： Radiomics Deep learning Computed tomography Colorectal liver metastases Virtual clinical trials Image reconstruction

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Electrohysterogram Data Augmentation Using Generative Adversarial Network for Pregnancy Outcome Prediction

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IEEE Access 2025年 13卷 92468-92482页

作者： Cheema, Muhammad Omar Din, Zia Mohy Ud Imran, Azhar Khan, Fawad Salam Al-Rawi, Mahmood Basil A. El-Meligy, Mohammed Gul, Jahan Zeb Air University Department of Biomedical Engineering Islamabad44000 Pakistan Air University Faculty of Computing & AI Department of Creative Technologies Islamabad44000 Pakistan King Saud University College of Applied Medical Sciences Department of Optometry Riyadh Saudi Arabia Amman Arab University College of Computer Science and Informatics Amman Jordan Maynooth University Department of Electronic Engineering Ireland

There are many difficulties in managing and detecting preterm pregnancies, especially in the early stages. Analyzing electrohysterogram data, which show the electrical activity of uterine muscles, is a promising non-invasive method for classifying term and preterm pregnancies. However, the effectiveness of machine learning classifiers is hampered by the inherent class imbalance in datasets. In order to overcome this constraint, this study uses a Generative Adversarial Network to produce high-quality synthetic EHG data while maintaining the statistical properties of actual signals. By successfully reducing class imbalance, the enhanced dataset makes it possible for machine learning models to be trained with resilience. Real and synthetic features did not differ significantly, according to statistical validation using a t-test (p > 0.05). With this method, the Random Forest classifier obtained a ROC-AUC of 0.99 and a 10-fold cross-validation accuracy of 98%. The suggested GAN-based method’s simplicity and efficacy demonstrate its promise for resolving class imbalance and developing reliable classification systems for diagnostic applications. © 2013 IEEE.

关键词： Generative adversarial networks

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Multimodal Contrastive Learning for Protein–Protein Interaction Inhibitor Prediction

Multimodal Contrastive Learning for Protein–Protein Interac...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Zitong Zhang Zhixian Wang Lingling Zhao Junjie Wang Chunyu Wang Faculty of Computing Harbin Institute of Technology Harbin China Department of Medical Informatics School of Biomedical Engineering and Informatics Nanjing Medical University Nanjing China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Protein-protein interactions (PPIs) are crucial for various cellular activities and disease development, and modulating PPIs using small molecule inhibitors (PPIIs) has gradually become a promising therapeutic strategy. Recently, researchers have proposed several machine learning methods to screen PPIIs, but most of the works focused on unimodal representations of molecules or combining multimodal features in a naive splicing manner. Meanwhile, current research progress is being slowed by the lack of large-scale PPII datasets. To address these issues, we propose MCLPPII, a unified multimodal contrastive learning framework for PPII prediction. MCLPPII extracts comprehensive molecular information from four modalities and effectively combines them through an adaptive feature fusion method. Furthermore, we propose a three-stage training strategy to enhance the PPII prediction capability of MCLPPII by leveraging self-supervised pre-training on a large unlabeled dataset. We evaluate MCLPPII on nine PPI targets and two downstream tasks, including the PPI inhibitor identification task and potency prediction task. Experimental results show that MCLPPII achieves competitive performance. The source code and datasets are freely available at https://***/1zzt/MCLPPII.

关键词： Proteins Training Protein engineering Inhibitors Splicing Source coding Contrastive learning Machine learning Feature extraction Diseases

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Comfort Shoulder (Rhomboid Major) Muscles Chair Identifier System 6

Comfort Shoulder (Rhomboid Major) Muscles Chair Identifier S...

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6th International Conference on Communications, Information System and Computer Engineering, CISCE 2024

作者： Moshayedi, Ata Jahangir Yu, Yuexin Savoji, Kimia Andani, Mehran Emadi School of Information Engineering Jiangxi University of Science and Technology Jiangxi Ganzhou China School of Computing Clemson University Biomedical Data Science and Informatics ClemsonSC United States University of Verona Department of Neurosciences Biomedicine and Movement Sciences Verona Italy

ISBN: (纸本)9798350353174

Nowadays, Sitting is one of the frequent activities of humans. particularly with the rise of office work, which is leading to an increase in the sitting periods for many individuals. Research highlights the importance of studying sitting posture to enhance seat quality, correct sitting methods, and prevent various health issues like neck and shoulder strains, etc. This article focuses on designing an IoT-based system that enables web-based data visualization without specialized software that can enable distance sampling. Through temperature sensors (DS18b20 and SHT11) and an FSR sensor, the system can identify distinct patterns in the main rhomboid muscles (the most important muscles of humans). The results show a significant relationship between individuals' body mass (BMI) and the observed muscle patterns, highlighting the importance of this correlation for researchers and industry designers alike. © 2024 IEEE.

关键词： Seats

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A Variational Autoencoder-Feed Forward Networks (VAE-FNN) Model for Classifying DNA Barcodes 1

A Variational Autoencoder-Feed Forward Networks (VAE-FNN) Mo...

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1st International Conference on Advances in Electrical, Electronics and Computational Intelligence, ICAEECI 2023

作者： Yadav, Abhivyakti Divya, S. Oviya, I.R. Raja, Kalpana Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India School of Medicine Yale University Section for Biomedical Informatics and Data Science New HavenCT United States

ISBN: (纸本)9798350342796

DNA Barcodes, which are particular fragments derived from brief sections of DNA (such as mitochondrial, nuclear, and plastid sequences), can be used to identify organisms from the major life kingdoms. In addition to supporting conventional taxonomic techniques, DNA barcoding is a potent tool that advances our knowledge of species diversity and their ecological functions. On a variety of organisms, the use of this approach for species categorization has been successful. In this paper, we examine how DNA barcoding has been used to classify species based on DNA barcodes as well as other related research that has been done over the years on the subject. After experimenting with a number of deep learning models, we have propose a Variational Auto Encoder + Feed Forward Neural Network workflow for classifiying species using DNA barcodes. The models have been assessed on the basis of performance factors including accuracy, recall, and precision. COI, rbcL, matK, and ITS are the specific gene sections that have been identified as barcodes. For both simulated and real datasets, the model can attain an average accuracy of greater than 95 percent. This DNA barcoding approach has the ability to simplify DNA barcode-based species identification and serve as a tool for species categorization. © 2023 IEEE.

关键词： DNA

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On the Effectiveness of Virtual IMU Data for Eating Detection with Wrist Sensors

On the Effectiveness of Virtual IMU Data for Eating Detectio...

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2022 ACM International Joint Conference on Pervasive and Ubiquitous computing and the 2022 ACM International Symposium on Wearable Computers, UbiComp/ISWC 2022

作者： Jain, Yash Kwon, Hyeokhyen Ploetz, Thomas School of Interactive Computing Georgia Institute of Technology AtlantaGA United States Department of Biomedical Informatics Emory University AtlantaGA United States

ISBN: (纸本)9781450394239

The successful training of human activity recognition (HAR) systems typically heavily depends on the availability of sufficient amounts of labeled sensor data. Unfortunately, obtaining large-scale labeled datasets is usually expensive and often limited by practical and / or privacy reasons. Recently, IMUTube was introduced to tackle this data scarcity problem through generating weakly-labeled, virtual IMU data from unconstrained video repositories, such as YouTube. IMUTube was demonstrated to be very effective at classifying locomotion or gym exercises that involve large movements of body parts. Yet, many important daily activities, such as eating, do not exhibit such substantial body (part) movements but are rather based on more subtle, fine-grained motions. This work explores the utility of IMUTube for such subtle motion activities with specific, exemplary application to eating detection. We found that-surprisingly-IMUTube is also very effective for this challenging HAR domain. Our experiment demonstrates that eating recognition systems benefit from virtual IMU data extracted from video datasets with significant improvements of recognition accuracy (increases of 8.4% and 5.9% F1-score absolute for both curated and in-the-wild video datasets, respectively relative to 71.5% F1-score of the baseline), which is encouraging for the broader use of systems like IMUTube. © 2022 Owner/Author.

关键词： Large dataset

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Cyber threat assessment and management for securing healthcare ecosystems using natural language processing

Cyber threat assessment and management for securing healthca...

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作者： Silvestri, Stefano Islam, Shareful Amelin, Dmitry Weiler, Gabriele Papastergiou, Spyridon Ciampi, Mario Institute for High Performance Computing and Networking of the National Research Council of Italy ICAR-CNR Via Pietro Castellino 111 Naples80131 Italy School of Computing and Information Science Anglia Ruskin University Cambridge United Kingdom Focal Point Waterloo Belgium Department of Informatics University of Piraeus Piraeus Greece Fraunhofer Institute for Biomedical Engineering IBMT Sulzbach Germany

The healthcare sectors have constantly faced significant challenge due to the rapid rise of cyber threats. These threats can pose any potential risk within the system context and disrupt the critical healthcare service delivery. It is therefore necessary for the healthcare organisations to understand and tackle the threats to ensure overall security and resilience. However, threats are continuously evolved and there is large amount of unstructured security-related textual information is available. This makes the threat assessment and management task very challenging. There are a number of existing works that consider Machine Learning models for detection and prediction of cyber attack but they lack of focus on the Natural Language Processing (NLP) to extract the threat information from unstructured security-related text. To this end, this work proposes a novel method to assess and manage threats by adopting natural language processing. The proposed method has been tailored for the healthcare ecosystem and allows to identify and assess the possible threats within healthcare information infrastructure so that appropriate control and mitigation actions can be taken into consideration to tackle the threat. In detail, NLP techniques are used to extract the useful threat information related to specific assets of the healthcare ecosystems from the largely available security-related information on Internet (e.g. cyber security news), to evaluate the level of the identified threats and to select the required mitigation actions. We have performed experiments on real healthcare ecosystems in Fraunhofer Institute for biomedical Engineering, considering in particular three different healthcare scenarios, namely implantable medical devices, wearables, and biobank, with the purpose of demonstrating the feasibility of our approach, which is able to provide a realistic manner to identify and assess the threats, evaluate the threat level and suggest the required mitigation actions. ©

关键词： Ecosystems

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Data Cleaning Process for mHealth Log Data to Inform Health Worker Performance

Data Cleaning Process for mHealth Log Data to Inform Health ...

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作者： Savai, Simon Muhindi Hasan, Md Kamrul Kamano, Jemimah Misoi, Lawrence Wakholi, Peter Were, Martin C. Institute of Biomedical Informatics Moi University Eldoret Kenya Department of Computer Science Vanderbilt University TN United States School of Medicine Moi University Eldoret Kenya Moi Teaching and Referral Hospital Eldoret Kenya School of Computing and Informatics Technology Makerere University Kampala Uganda Department of Biomedical Informatics VUMC Nashville TN United States

ISBN: (纸本)9781643682907

Log data, captured during use of mobile health (mHealth) applications by health providers, can play an important role in informing nature of user engagement with the application. The log data can also be employed in understanding health provider work patterns and performance. However, given that these logs are raw data, they require robust cleaning and curation if accurate conclusions are to be derived from analyzing them. This paper describes a systematic data cleaning process for mHealth-derived logs based on Broeck's framework, which involves iterative screening, diagnosis, and treatment of the log data. For this study, log data from the demonstrative mUzima mHealth application are used. The employed data cleaning process uncovered data inconsistencies, duplicate logs, missing data within logs that required imputation, among other issues. After the data cleaning process, only 39,229 log records out of the initial 91,432 usage logs (42.9%) could be included in the final dataset suitable for analyses of health provider work patterns. This work highlights the significance of having a systematic data cleaning approach for log data to derive useful information on health provider work patterns and performance. © 2022 The authors and IOS Press.

关键词： Developing countries

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ScisTree2: An Improved Method for Large-Scale Inference of Cell Lineage Trees and Genotype Calling from Noisy Single Cell Data 29th

ScisTree2: An Improved Method for Large-Scale Inference of...

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29th International Conference on Research in Computational Molecular Biology, RECOMB 2025

作者： Zhang, Haotian Zhang, Yiming Gao, Teng Wu, Yufeng School of Computing University of Connecticut StorrsCT06269 United States Department of Biomedical Informatics Harvard Medical School BostonMA United States

ISBN: (纸本)9783031902512

Cell lineage tree is a model for the evolutionary history of cells sampled from an organism. Inference of cell lineage trees has been made possible by recent developments in single-cell sequencing. Accurate inference of cell lineage tree from large single cell data is a computational challenge. In this paper, we present ScisTree2, a fast and accurate cell lineage tree inference and genotype calling approach based on the infinite sites model. Compared to the original ScisTree, the main new feature of ScisTree2 is an accurate and efficient local search approach based on subtree prune and regraft (SPR) that is not implemented in the original ScisTree. To the best of our knowledge, ScisTree2 is the first model-based cell lineage tree inference and genotype calling approach that can work with very large data with, e.g., tens of thousands of cells. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Cell culture

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Tomato Disease Classification Using CNN 4th

Tomato Disease Classification Using CNN

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4th International Conference on Smart Systems: Innovations in computing, SSIC 2023

作者： Archanaa, N. Daniel, V. Vifert Jenuben Divya, S. Raja, Kalpana Oviya, I.R. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Department of Sciences Amrita School of Engineering Amrita Vishwa Vidyapeetham Chennai India Section for Biomedical Informatics and Data Science School of Medicine Yale University New HavenCT United States

ISBN: (纸本)9789819736898

Plant disease detection using various machine learning techniques is a very popular research area. Many promising results have been obtained, but only a few specific applications can still make farmers’ lives easier. This research focuses on the important task of identifying tomato diseases at an early stage to prevent their spread and protect crop yields more broadly, emphasizing the idea that disease prevention will more important than treatment. Leveraging the capabilities of convolutional neural networks (CNN), we developed a model that diagnoses eight distinct tomato diseases using only their images. Diseases listed include bacterial spot, early blight, late blight, leaf mold, septoria leaf spot, two-spotted spider mite, target spot, and tomato yellow leaf curl virus. An integral part of our approach involves sophisticated image prepossessing and enhancement techniques, ensuring data quality and model robustness. Feature extraction plays a key role in transforming raw image data into recognizable patterns. The architecture of the CNN has been meticulously designed, consisting of multiple layers with a notable focus on the ReLU activation function and other relevant hyper-parameters. Our main dataset comes from PlantVillage, known for synthesizing rich and diverse plant images. After rigorous training and validation, the CNN model showed exceptional accuracy of 96.88% in tomato disease identification. However, challenges such as overfitting, image quality variability, and class imbalances were encountered but were resolved through strategic data augmentation, careful CNN overlay. In summary, our research presents a practical solution for farmers and agricultural entities. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Fruits

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