Article
Engineering, Electrical & Electronic
Bangjun Li, Pengfei Zhang, Yankun Cao, Longkun Sun, Jianqin Feng, Yuezhong Zhang, Qi Yang, Yujun Li, Zhi Liu
Summary: The Intravascular Ultrasound (IVUS) technology is widely used in clinical practice for the diagnosis of coronary artery disease. However, the mechanical rotating imaging system used in IVUS often suffers from guidewire artifacts, which hinders the visualization and subsequent evaluation of tissue structure. In this paper, the researchers proposed a deep learning based network named AIVUS to repair the corrupted IVUS images and improve the restoration capability. The network utilizes spatial and temporal information to recover the high-fidelity original content and maintain consistency between frames. Results show that the proposed method outperforms other restoration models and has potential clinical value.
IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING
(2022)
Article
Computer Science, Information Systems
Dennies Tsietso, Abid Yahya, Ravi Samikannu, Muhammad Usman Tariq, Muhammad Babar, Basit Qureshi, Anis Koubaa
Summary: Breast cancer is a leading cause of death among women worldwide, and early detection is crucial for reducing mortality. This paper proposes a novel computer-aided diagnosis (CADx) system that uses deep learning techniques and incorporates multiple breast thermogram views and patient clinical data to improve accuracy. The system outperforms single-input models and achieves an overall accuracy of 90.48%, a sensitivity of 93.33%, and an AUROC curve of 0.94. This approach could provide a more cost-effective and less hazardous screening option for breast cancer detection, particularly for diverse age groups.
Article
Neurosciences
Liyong Luo, Yuanxu Xu, Junxia Pan, Meng Wang, Jiangheng Guan, Shanshan Liang, Yurong Li, Hongbo Jia, Xiaowei Chen, Xingyi Li, Chunqing Zhang, Xiang Liao
Summary: Two-photon Ca2+ imaging is a leading technique for recording neuronal activities in vivo, but often suffers from corruption due to complex noises during experiments. A neural network approach combining spatiotemporal filtering and model blind learning is proposed for image denoising and efficient restoration of imaging data. The method outperforms current state-of-the-art methods in quantitatively evaluating the quality of denoising performance, providing a valuable tool for denoising two-photon Ca2+ imaging data with model blind spatiotemporal processing.
FRONTIERS IN NEUROSCIENCE
(2021)
Review
Computer Science, Information Systems
Guangsheng Chen, Hailiang Lu, Weitao Zou, Linhui Li, Mahmoud Emam, Xuebin Chen, Weipeng Jing, Jian Wang, Chao Li
Summary: Remote sensing images have been widely used in Earth observation tasks, but a single sensor cannot provide observational images with both high spatial and temporal resolution. The spatiotemporal fusion (STF) method has been proposed to overcome this constraint. Many STF methods have been proposed based on different principles and strategies. A new review is needed to reflect the current research status. This review provides a comprehensive overview of current advances, discusses the basic principles and limitations, and collects recent applications. It also introduces publicly available resources and quantitative metrics, and discusses open problems and challenges for future attention.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2023)
Article
Oncology
Laith Alzubaidi, Muthana Al-Amidie, Ahmed Al-Asadi, Amjad J. Humaidi, Omran Al-Shamma, Mohammed A. Fadhel, Jinglan Zhang, J. Santamaria, Ye Duan
Summary: Deep learning in medical image analysis faces challenges such as data scarcity and annotation difficulties. The proposed approach utilizes transfer learning and a novel deep convolutional neural network model to tackle these issues, achieving significant performance improvements in skin and breast cancer classification tasks.
Article
Environmental Sciences
Chongya Jiang, Kaiyu Guan, Yizhi Huang, Maxwell Jong
Summary: This study presents the Field Rover method, which uses vehicle-mounted cameras to collect ground truth data on crop harvesting status. The machine learning approach and remote sensing technology are employed to upscale the results to a regional scale. The accuracy of the remote sensing method in predicting crop harvesting dates is validated through comparison with satellite data.
REMOTE SENSING OF ENVIRONMENT
(2024)
Review
Engineering, Biomedical
Shanshan Wang, Taohui Xiao, Qiegen Liu, Hairong Zheng
Summary: Magnetic resonance imaging is a powerful imaging modality that can be accelerated using deep learning to provide accurate image reconstructions. By replacing human-defined signal models with models learned from data, the speed and accuracy of MR imaging techniques can be enhanced.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2021)
Article
Radiology, Nuclear Medicine & Medical Imaging
Pierre-Antoine Bornet, Nicolas Villani, Romain Gillet, Edouard Germain, Charles Lombard, Alain Blum, Pedro Augusto Gondim Teixeira
Summary: This study evaluated the image quality and clinical acceptance of a deep learning reconstruction (DLR) algorithm compared to traditional iterative reconstruction (IR) algorithms. The results showed that DLR had lower noise and higher low-contrast detectability index at lower dose levels. DLR also had higher spatial resolution and lower noise compared to the other IR algorithms. Radiologists generally preferred the DLR images.
EUROPEAN RADIOLOGY
(2022)
Article
Chemistry, Multidisciplinary
Hyesook Son, Seokyeon Kim, Hanbyul Yeon, Yejin Kim, Yun Jang, Seung-Eock Kim
Summary: The output of a deep-learning model varies depending on the input characteristics, and it is important to analyze how these characteristics affect prediction results. In order to interpret the output of deep learning models, a visualization system has been proposed to visualize predictions based on input characteristics.
APPLIED SCIENCES-BASEL
(2021)
Article
Engineering, Biomedical
Bjoern Menze, Fabian Isensee, Roland Wiest, Bene Wiestler, Klaus Maier-Hein, Mauricio Reyes, Spyridon Bakas
Summary: The quantitative analysis of brain tumor images using computational tools, particularly machine learning and deep learning algorithms, has become increasingly popular. This review focuses on diagnostic biomarkers for glioma and publicly available resources, with an emphasis on the Multimodal Brain Tumor Segmentation (BraTS) Challenge. It also discusses state-of-the-art methods in glioma image segmentation, highlighting publicly available tools and deep learning algorithms.
COMPUTERIZED MEDICAL IMAGING AND GRAPHICS
(2021)
Article
Computer Science, Interdisciplinary Applications
Jing Cheng, Zhuo-Xu Cui, Wenqi Huang, Ziwen Ke, Leslie Ying, Haifeng Wang, Yanjie Zhu, Dong Liang
Summary: A new DL-based approach termed Learned DC is proposed in this work, which implicitly learns data consistency for MR image reconstruction. The method is evaluated with highly undersampled dynamic data and outperforms the state-of-the-art in both quantitative and qualitative performance.
IEEE TRANSACTIONS ON MEDICAL IMAGING
(2021)
Article
Automation & Control Systems
Xuanying Zhang, Yuzhu Wang, Lianjing Wei, Jinrong Jiang, Pengfei Lin, Hailong Liu
Summary: El Nino/Southern Oscillation (ENSO) is a complex event that affects the equatorial Pacific Ocean, causing abnormal Sea Surface Temperature (SST) changes. Accurate long-term ENSO predictions are crucial for global climate extremes and ecosystem stability. We have developed innovative methods, including transfer learning with simulated data, a new encoder-decoder structure with spatiotemporal memory cells, and mixed-precision computing to improve the accuracy and efficiency of ENSO forecasting. Our model outperforms existing statistical models and demonstrates precise predictions for El Nino, La Nina, and neutral ENSO conditions.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Biochemical Research Methods
Wanqiu Zhang, Marc Claesen, Thomas Moerman, M. Reid Groseclose, Etienne Waelkens, Bart De Moor, Nico Verbeeck
Summary: This paper introduces the use of neural networks to extract high-level features from ion images and their application in ion image clustering. The results show that neural network-based pipelines provide better clustering outcomes and introduce a relative isotope ratio metric for clustering evaluation.
ANALYTICAL AND BIOANALYTICAL CHEMISTRY
(2021)
Article
Radiology, Nuclear Medicine & Medical Imaging
Robin Wang, Zhicheng Jiao, Li Yang, Ji Whae Choi, Zeng Xiong, Kasey Halsey, Thi My Linh Tran, Ian Pan, Scott A. Collins, Xue Feng, Jing Wu, Ken Chang, Lin-Bo Shi, Shuai Yang, Qi-Zhi Yu, Jie Liu, Fei-Xian Fu, Xiao-Long Jiang, Dong-Cui Wang, Li-Ping Zhu, Xiao-Ping Yi, Terrance T. Healey, Qiu-Hua Zeng, Tao Liu, Ping-Feng Hu, Raymond Y. Huang, Yi-Hui Li, Ronnie A. Sebro, Paul J. L. Zhang, Jianxin Wang, Michael K. Atalay, Wei-Hua Liao, Yong Fan, Harrison X. Bai
Summary: This study successfully developed an artificial intelligence system that integrates chest CT and clinical data to predict the risk of future deterioration to critical illness in COVID-19 patients. The AI system accurately triaged patients, identified high-risk individuals, and guided personalized treatment.
EUROPEAN RADIOLOGY
(2022)
Article
Computer Science, Information Systems
Dong Zhang, Wentai Pang, Keyi Wang, Fengwen Yang, Junhua Zhang
Summary: Tongue diagnosis is an important tool in the diagnosis and evaluation of Traditional Chinese Medicine. However, the lack of spectral information in color tongue images may lead to the lack of key information in diagnosis. Hyperspectral images can provide rich spectral information and accurately describe tongue coating. This study conducted feature extraction and analysis on hyperspectral images of different tongue coatings and proposed a deep learning framework for classification and quantitative recognition based on spectral-spatial features.
Article
Genetics & Heredity
Sarah Verheyen, Jasmin Blatterer, Michael R. Speicher, Gandham SriLakshmi Bhavani, Geert-Jan Boons, Mai-Britt Ilse, Dominik Andrae, Jens Spross, Frederic Maxime Vaz, Susanne G. Kircher, Laura Posch-Pertl, Daniela Baumgartner, Torben Luebke, Hitesh Shah, Ali Al Kaissi, Katta M. Girisha, Barbara Plecko
Summary: This study reports four affected individuals from two unrelated consanguineous families with MPS-related features, showing additional cardiac and ophthalmological abnormalities in some cases. The detection of mild elevation of a specific GAG in some subjects suggests a novel subtype of mucopolysaccharidosis, which is proposed to be named subtype X.
JOURNAL OF MEDICAL GENETICS
(2022)
Article
Cell Biology
Sonja Langthaler, Jasmina Lozanovic Sajic, Theresa Rienmueller, Seth H. Weinberg, Christian Baumgartner
Summary: The mathematical modeling of ion channel kinetics is crucial for understanding electrophysiological mechanisms. In this paper, a new system theory-based approach is introduced, using transfer function characterization and patch clamp measurements, which shows exceptional accuracy and computational efficiency compared to traditional methods. This method has the potential to improve cell and organ model simulations and is a valuable tool for next-generation electrophysiology.
Article
Engineering, Biomedical
Christoph Leitner, Robert Jarolim, Bernhard Englmair, Annika Kruse, Karen Andrea Lara Hernandez, Andreas Konrad, Eric Yung-Sheng Su, Joerg Schroettner, Luke A. Kelly, Glen A. Lichtwark, Markus Tilp, Christian Baumgartner
Summary: Biomechanical and clinical gait research use machine learning to track muscle-tendon junctions, providing support in gait analysis. Extensive data collection and deep learning training showed that the model achieved similar performance in identifying junction position compared to human experts, and it is 100 times faster than manual labeling.
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
(2022)
Review
Chemistry, Analytical
Yasmine Heyer, Daniela Baumgartner, Christian Baumgartner
Summary: Understanding the range and limits of human trans thoracic impedance (TTI) is crucial for cardiac defibrillator testing and design. A literature survey based on 71 selected articles revealed that TTI varied from 12 to 212 Ohms, with an average TTI of 76.7 Ohms. Factors such as shock waveforms and protocols, coupling devices, electrode characteristics, and patient characteristics were found to influence TTI, with coupling devices, electrode size, and pressure having the greatest impact.
Article
Materials Science, Multidisciplinary
Tony Schmidt, Marie Jakesova, Vedran Derek, Karin Kornmueller, Oleksandra Tiapko, Helmut Bischof, Sandra Burgstaller, Linda Waldherr, Marta Nowakowska, Christian Baumgartner, Muammer Ucal, Gerd Leitinger, Susanne Scheruebel, Silke Patz, Roland Malli, Eric Daniel Glowacki, Theresa Rienmueller, Rainer Schindl
Summary: This research evaluates the performance of organic electrolytic photocapacitor (OEPC) optoelectronic stimulators on single mammalian cells and neurons, demonstrating the ability to manipulate channel conductivity and trigger action potentials with millisecond precision. This opens up possibilities for novel in vitro electrophysiology protocols and potential in vivo applications.
ADVANCED MATERIALS TECHNOLOGIES
(2022)
Editorial Material
Oncology
Christian Baumgartner
Summary: The introduction of functional in-silico models opens up new possibilities in cancer research and drug development. The digital twin of A549 cell's electrophysiology allows investigation of ion channel function and prediction of cell cycle progression changes. The model provides a platform for verifying research hypotheses and optimizing experimental methods.
JOURNAL OF EXPERIMENTAL & CLINICAL CANCER RESEARCH
(2022)
Article
Computer Science, Interdisciplinary Applications
A. Lara-Hernandez, T. Rienmueller, I. Juarez, M. Perez, F. Reyna, D. Baumgartner, V. N. Makarenko, O. L. Bockeria, M. Maksudov, R. Rienmueller, C. Baumgartner
Summary: This article introduces a deep learning-based deformable image registration method for quantitative myocardial perfusion CT examinations in dynamic cardiac cycles. The method addresses unique challenges such as low image quality, inaccurate anatomical landmarks, dynamic changes in contrast agent concentration, and misalignment caused by cardiac stress, respiration, and patient motion. The proposed method reduces local tissue displacements of the left ventricle and demonstrates fast processing time compared to conventional methods, making it suitable for daily clinical routine.
IEEE TRANSACTIONS ON MEDICAL IMAGING
(2023)
Article
Physiology
Mathias Polz, Katharina Bergmoser, Martin Horn, Michael Schoerghuber, Jasmina Lozanovic, Theresa Rienmueller, Christian Baumgartner
Summary: This study presents a novel digital model for predicting cumulative fluid balance (CFB) based on system theory. The model utilizes recorded patient fluid data as the sole parameter source and applies the concept of a transfer function. The results demonstrate that a combination of 48-hour estimation time and 8-16-hour prediction time achieves high accuracy in CFB prediction. This study provides a promising proof of principle for developing computational models for fluid prediction that do not require large datasets.
FRONTIERS IN PHYSIOLOGY
(2023)
Article
Materials Science, Multidisciplinary
Kirill Keller, Christoph Leitner, Christian Baumgartner, Luca Benini, Francesco Greco
Summary: We present the fabrication of a fully printed, lead-free, polymer piezoelectric transducer and demonstrate the characterization of its structural, dielectric, and ferroelectric properties at different processing stages. The performance of the transducer made with poly(vinylidene fluoride-trifluoroethylene) is evaluated through resonance frequency analyses, acoustic power measurements, and pulse-echo experiments. Importantly, we demonstrate an optimal performance in the medical ultrasound range (<15 MHz) with acoustic power >1 W cm(-2) for the first time in a fully printed transducer, which shows promise for applications in epidermal and wearable electronics. Overall, these findings lay a strong foundation for future research in the field of flexible ultrasound transducers.
ADVANCED MATERIALS TECHNOLOGIES
(2023)
Editorial Material
Oncology
Christian Baumgartner, Daniela Baumgartner
CLINICAL AND TRANSLATIONAL MEDICINE
(2023)
Article
Biology
Seyyed Bahram Borgheai, Alyssa Hillary Zisk, John McLinden, James Mcintyre, Reza Sadjadi, Yalda Shahriari
Summary: This study proposed a novel personalized scheme using fNIRS and EEG as the main tools to predict and compensate for the variability in BCI systems, especially for individuals with severe motor deficits. By establishing predictive models, it was found that there were significant associations between the predicted performances and the actual performances.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Hongliang Guo, Hanbo Liu, Ahong Zhu, Mingyang Li, Helong Yu, Yun Zhu, Xiaoxiao Chen, Yujia Xu, Lianxing Gao, Qiongying Zhang, Yangping Shentu
Summary: In this paper, a BDSMA-based image segmentation method is proposed, which improves the limitations of the original algorithm by combining SMA with DE and introducing a cooperative mixing model. The experimental results demonstrate the superiority of this method in terms of convergence speed and precision compared to other methods, and its successful application to brain tumor medical images.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Jingfei Hu, Linwei Qiu, Hua Wang, Jicong Zhang
Summary: This study proposes a novel semi-supervised point consistency network (SPC-Net) for retinal artery/vein (A/V) classification, addressing the challenges of specific tubular structures and limited well-labeled data in CNN-based approaches. The SPC-Net combines an AVC module and an MPC module, and introduces point set representations and consistency regularization to improve the accuracy of A/V classification.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Omair Ali, Muhammad Saif-ur-Rehman, Tobias Glasmachers, Ioannis Iossifidis, Christian Klaes
Summary: This study introduces a novel hybrid model called ConTraNet, which combines the strengths of CNN and Transformer neural networks, and achieves significant improvement in classification performance with limited training data.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Juan Antonio Valera-Calero, Dario Lopez-Zanoni, Sandra Sanchez-Jorge, Cesar Fernandez-de-las-Penas, Marcos Jose Navarro-Santana, Sofia Olivia Calvo-Moreno, Gustavo Plaza-Manzano
Summary: This study developed an easy-to-use application for assessing the diagnostic accuracy of digital pain drawings (PDs) compared to the classic paper-and-pencil method. The results demonstrated that digital PDs have higher reliability and accuracy compared to paper-and-pencil PDs, and there were no significant differences in assessing pain extent between the two methods. The PAIN EXTENT app showed good convergent validity.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Biao Qu, Jialue Zhang, Taishan Kang, Jianzhong Lin, Meijin Lin, Huajun She, Qingxia Wu, Meiyun Wang, Gaofeng Zheng
Summary: This study proposes a deep unrolled neural network, pFISTA-DR, for radial MRI image reconstruction, which successfully preserves image details using a preprocessing module, learnable convolution filters, and adaptive threshold.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Alireza Rafiei, Milad Ghiasi Rad, Andrea Sikora, Rishikesan Kamaleswaran
Summary: This study aimed to improve machine learning model prediction of fluid overload by integrating synthetic data, which could be translated to other clinical outcomes.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Jinlian Ma, Dexing Kong, Fa Wu, Lingyun Bao, Jing Yuan, Yusheng Liu
Summary: In this study, a new method based on MDenseNet is proposed for automatic segmentation of nodular lesions from ultrasound images. Experimental results demonstrate that the proposed method can accurately extract multiple nodules from thyroid and breast ultrasound images, with good accuracy and reproducibility, and it shows great potential in other clinical segmentation tasks.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Jiabao Sheng, SaiKit Lam, Jiang Zhang, Yuanpeng Zhang, Jing Cai
Summary: Omics fusion is an important preprocessing approach in medical image processing that assists in various studies. This study aims to develop a fusion methodology for predicting distant metastasis in nasopharyngeal carcinoma by mitigating the disparities in omics data and utilizing a label-softening technique and a multi-kernel-based neural network.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Zhenxiang Xiao, Liang He, Boyu Zhao, Mingxin Jiang, Wei Mao, Yuzhong Chen, Tuo Zhang, Xintao Hu, Tianming Liu, Xi Jiang
Summary: This study systematically investigates the functional connectivity characteristics between gyri and sulci in the human brain under naturalistic stimulus, and identifies unique features in these connections. This research provides novel insights into the functional brain mechanism under naturalistic stimulus and lays a solid foundation for accurately mapping the brain anatomy-function relationship.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Qianqian Wang, Mingyu Zhang, Aohan Li, Xiaojun Yao, Yingqing Chen
Summary: The development of PARP-1 inhibitors is crucial for the treatment of various cancers. This study investigates the structural regulation of PARP-1 by different allosteric inhibitors, revealing the basis of allosteric inhibition and providing guidance for the discovery of more innovative PARP-1 inhibitors.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Qing Xu, Wenting Duan
Summary: In this paper, a dual attention supervised module, named DualAttNet, is proposed for multi-label lesion detection in chest radiographs. By efficiently fusing global and local lesion classification information, the module is able to recognize targets with different sizes. Experimental results show that DualAttNet outperforms baselines in terms of mAP and AP50 with different detection architectures.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Kaja Gutowska, Piotr Formanowicz
Summary: The primary aim of this research is to propose algorithms for identifying significant reactions and subprocesses within biological system models constructed using classical Petri nets. These solutions enable two analysis methods: importance analysis for identifying critical individual reactions to the model's functionality and occurrence analysis for finding essential subprocesses. The utility of these methods has been demonstrated through analyses of an example model related to the DNA damage response mechanism. It should be noted that these proposed analyses can be applied to any biological phenomenon represented using the Petri net formalism. The presented analysis methods extend classical Petri net-based analyses, enhancing our comprehension of the investigated biological phenomena and aiding in the identification of potential molecular targets for drugs.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Hansle Gwon, Imjin Ahn, Yunha Kim, Hee Jun Kang, Hyeram Seo, Heejung Choi, Ha Na Cho, Minkyoung Kim, Jiye Han, Gaeun Kee, Seohyun Park, Kye Hwa Lee, Tae Joon Jun, Young-Hak Kim
Summary: Electronic medical records have potential in advancing healthcare technologies, but privacy issues hinder their full utilization. Deep learning-based generative models can mitigate this problem by creating synthetic data similar to real patient data. However, the risk of data leakage due to malicious attacks poses a challenge to traditional generative models. To address this, we propose a method that employs local differential privacy (LDP) to protect the model from attacks and preserve the privacy of training data, while generating medical data with reasonable performance.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Siwei Tao, Zonghan Tian, Ling Bai, Yueshu Xu, Cuifang Kuang, Xu Liu
Summary: This study proposes a transfer learning-based method to address the phase retrieval problem in grating-based X-ray phase contrast imaging. By generating a training dataset and using deep learning techniques, this method improves image quality and can be applied to X-ray 2D and 3D imaging.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)