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Search Results: cmim
Current Medical Imaging
ISSN: 1875-6603
Impact Factor :
1.1
CiteScore:
2.6
- Cardiac Imaging
- Computed Tomography
- Computer-aided Diagnosis
- Machine Vision in Medicine
- Magnetic Resonance
- Medical Image Visualization
- Medical Imaging and Analysis
- Molecular Imaging
- Musculoskeletal Imaging
- Nuclear Medicine
- Pattern Recognition in Medical Images
- Pre-clinical Imaging
- Vascular and Interventional Radiology
- Women's and Pediatric Imaging
- X-ray and Abdominal Imaging
- Other related areas
Editor-in-Chief:
University of California
Irvine, CA
USA
Special Issues With Active Call for Papers
Advanced Feature Extraction Techniques in Medical Imaging for Early Diagnosis of Complex Diseases.
Medical images are located and retrieved by convolution neural networks. It extracts powerful picture features for label description using deep learning technology. It then sends the relevant parameters to execute tag matching and identify the photos as queries. A variety of techniques, including statistical analysis, pre trained models, and mathematical transformations, can be used for feature extraction. Standard methods for processing images include texture analysis, edge detection, and colour based approaches. It obtains new features... see more
Deep Learning in Neuroimaging
Introduction – Deep learning has revolutionized medical imaging by offering automated, high-precision analytical methods that transform diagnostic processes. This proposal outlines a topical collection dedicated to “Deep Learning in Neuroimaging,” emphasizing its critical role in enhancing diagnostic accuracy, early disease detection, and personalized treatment strategies in neurological disorders. Importance of the Topic - Neuroimaging is integral to the early diagnosis and management of conditions such as brain tumors, Alzheimer’s disease, and multiple sclerosis. Recent advances... see more
Generative AI and Machine Learning for High-Resolution Medical Image Analysis
Generative AI is revolutionizing medical imaging by producing high-resolution, synthetic yet realistic images that enhance disease diagnosis, monitoring, and treatment planning. By leveraging deep learning and neural networks, these models improve image quality, reduce noise, and support anomaly detection. Additionally, AI-driven approaches enable predictive analysis, aiding in disease progression forecasting and automated image interpretation. Beyond imaging, generative AI enhances natural language processing (NLP) applications, including medical report generation and multilingual healthcare accessibility. However, ethical and... see more
Ultrahigh Field Magnetic Resonance Imaging for GIST Interpretation and Diagnostic Measurements
A non-invasive imaging technique termed magnetic resonance imaging (MRI) creates detailed, three-dimensional anatomy images. It is frequently employed in the diagnosis, monitoring, and detection of diseases. MRIs use magnets to create three-dimensional images of the body’s internal structures. It can draw attention to alterations in bodily tissue that point to damage. In contrast, an ultrasound creates images of the body’s internal organs and structures using high-frequency sound waves. A diagnostic examination called magnetic resonance imaging... see more
Generative AI for Data Augmentation in Small Medical Imaging Datasets
Opportunities exist to automate and enhance the precision of medical picture analysis through the use of generative AI. Picture segmentation, picture synthesis, patient outcome prediction, and anomaly detection are among the applications of Generation AI. Data security and privacy are two major issues that businesses may run into when generative AI is being implemented. Large data sets are essential for generative AI models to produce precise and significant results. But managing such vast amounts of... see more
Integration of Artificial Intelligence in Diagnostic Imaging in health care
The integration of Artificial Intelligence (AI) into diagnostic medical imaging is revolutionizing healthcare through improving the accuracy as well as efficiency of medical diagnoses. AI-based algorithms can swiftly analyze the complex biomedical images, viz., X-rays, MRIs, and CT scans for detecting patterns and abnormalities that might be challenging for human eyes to determine, facilitating early disease detection along with intervention. This advancement not only enhances the patient outcomes but also streamlines the workflow within the... see more
Multimodal Imaging: Integrating Techniques for Comprehensive Diagnostics
The anatomical, physiological, biochemical, and genetic data provided by today's medical imaging technology should aid in precise illness diagnosis, treatment response prediction, and the creation of extremely sensitive and targeted medications and imaging agents. Nevertheless, comprehensive medical imaging is not provided by any of the existing human imaging techniques. Multimodality imaging has emerged as a compelling approach for a way to use the advantages of various imaging modalities. Combining radio, nuclear, and optical techniques is... see more
Next-Generation Healthcare and Secure Medical Imaging: Advances in Authentication, Privacy, and Data Management
Background and Motivation: The rapid digitization of healthcare systems has driven an unprecedented reliance on Medical Imaging (MI) technologies such as X-rays, Ultrasound, Computed Tomography (CT), and Medical Resonance Imaging (MRI) for accurate diagnosis, treatment planning, and disease monitoring. With the proliferation of telemedicine and Internet of Medical Things (IoMT) devices, medical images are increasingly transmitted, analyzed, and stored across interconnected networks. While this shift enables real-time collaboration and improved patient outcomes, it also raises... see more
Deep Learning for 3D Medical Image Reconstruction in Surgical Assistance
Deep learning, a subset of machine learning, utilizes artificial neural networks to analyze complex data, drawing inspiration from the human brain. In medical imaging, deep learning is pivotal for tasks like diagnosing diseases, classifying lesions, and predicting cancer risks. Techniques like convolutional neural networks (CNNs) enhance precision in medical image processing, overcoming challenges such as inconsistent lighting, low resolution, and radiation risks in imaging modalities like X-rays, CT scans, and MRIs. Deep learning models can... see more
Distributed Edge Networks: Federated Learning for Safe and Effective Medical Image Diagnosis
The cooperative and decentralized technique of federated learning removes the need for local data exchange. Instead, it is used for local model training, which sends only the model parameters to a central server. By building an objective global model that conforms to local models while maintaining user privacy, Federated Learning (FL), a distributed machine learning model, preserves user privacy. In order to improve the efficiency and privacy of heterogeneous information in public health records and... see more
Multimedia and Image/Video Processing for Advanced Medical Education and Training
In the scope of developed medical education and training, multimedia and image or video processing plays an important role in improving learning experiences, enhancing practical skills and understanding. Legacy methods of medical education often trusted on textbooks, lectures and hands-on experiences, but the incorporation of multimedia and image/video processing provides powerful and interactive tools to increase these approaches. Intricate anatomical structures and physiological processes can be understood with the help of interactive visualizations and simulations.... see more
Emerging Trends and Techniques in Medical Imaging Using Machine Learning and Deep Learning Techniques
This special issue will aim to discuss different approaches, initiatives, and applications in medical imaging fields (including machine intelligence, mining engineering, modeling and simulation, computer, communication, networking and information engineering, systems engineering, innovative computing systems, adaptive technologies for sustainable growth, and theoretical and applied sciences). This collection should inspire various scholars to contribute research on intelligence principles and approaches in their respective research communities, while enriching the body of research on computational intelligence. see more
Deep Learning for Advanced Image Analysis in Health Informatics
The field of health informatics has endured a sharp increase in the use of image analysis due to the abundance of versatile data. This has also led to a rise in fascination with the development of computational models based on information and deep learning in the field of health informatics. With its roots in computerised neural networks, deep learning (DL) is a machine learning approach that has gained significant traction recently and holds the potential... see more
AI-Driven Diagnostics in Radiology and Medical Imaging
Medical imaging data, such as X-rays, MRIs, ultrasounds, CT scans, and DXAs, can be analysed by AI algorithms to help healthcare professionals identify and diagnose diseases more rapidly and accurately. Among the most cutting-edge applications of artificial intelligence (AI) in healthcare are imaging-related applications like cancer detection. AI could revolutionise illness prevention, early diagnosis, and treatment, enabling the NHS to deliver better care and quicker access to treatment. AI also improves diagnosis accuracy. Artificial intelligence... see more
Intelligent Imaging: AI Applications in Enhancing Medical Image Quality and Diagnostic Accuracy
AI also improves the precision of diagnosis. Artificial intelligence (AI) systems can spot patterns and abnormalities in medical imaging databases that the human eye would miss. Reducing misdiagnoses and guaranteeing that patients receive the appropriate treatment on time depend heavily on this improved accuracy. Nevertheless, there are difficulties in integrating AI with diagnostic imaging. Some of the obstacles that need to be overcome are worries about data privacy, possible biases in AI algorithms, and the... see more
Deep Learning Approaches for Image Enhancement in Medical Diagnostics
The medical and healthcare sectors are very different from one another. Consumers in these sectors need more affordable offerings. Health or medical specialists are typically the ones who analyze medical data. In particular, medical imaging evaluation heavily relies on machine learning and deep learning. They not only yield outstanding outcomes in various practical applications, but they also demonstrate strong precision in the interpretation and diagnosis of medical images. The health care industry differs greatly from... see more
Advancements and Applications of Photon Counting CT: Transforming Imaging Precision and Quantification
Photon Counting Computed Tomography (CT) represents a transformative advancement in medical imaging technology, offering unprecedented levels of precision and diagnostic capability. This special issue will explore the latest innovations and applications of photon counting CT, highlighting its advantages over traditional energy-integrating detectors. The scope includes an in-depth examination of its impact on image quality, spatial resolution, and spectral discrimination, as well as its role in advancing quantitative imaging techniques. The significance of this topic lies... see more
Generative Adversarial Networks (GANs) for Synthetic Medical Image Generation
The quantity of data for training medical image-based diagnostic and therapy models is growing as deep machine learning advances. Because of their superior picture-generating capabilities, generative adversarial networks (GANs) have garnered interest in the field of medical image processing and have found widespread use in data augmentation. Automated algorithms that create data simulations by learning from distributions of probability for real data are known as generative adversarial networks, or GANs. Generator and discriminator models are... see more
Image-based Diagnosis and Treatment in Craniofacial Diseases
This special issue of "Current Medical Imaging" is dedicated to the rapidly developing field of image-based diagnosis and treatment of craniofacial disorders. It aims to provide clinicians and researchers with a comprehensive platform to share their latest findings, insights, and innovations in applying imaging technologies to diagnose, manage, and treat craniofacial disorders. see more
Recent Applications and Future Directions of Medical Image Analysis through Self-Supervised Learning
Self-supervised learning techniques use unlabeled samples to learn generalisations about multiple ideas, which enables downstream task learning that is annotation-efficient. Technological developments in deep learning and computational sensing offer intriguing approaches to medical image analysis that could enhance patient outcomes and healthcare delivery. Unfortunately, the current architecture for training deep learning models necessitates a significant amount of labelled training data, which is prohibitively expensive and time-consuming to curate for medical imaging. Because self-supervised learning can... see more
Deep Learning for Early Disease Detection in Medical Images
In the medical area, computer-aided detection through Deep Learning (DL) and Machine Learning (ML) is rapidly expanding. Medical pictures are thought to be the real source of the relevant data needed for disease diagnosis. One of the most crucial things to reduce the death rate from cancer and tumours is early disease detection using a variety of modalities. Radiologists and medical professionals can better understand the internal anatomy of a discovered disease by using modalities... see more
Applying medical image processing techniques for early cancer detection in mammograms
Breast cancer survival and successful treatment are significantly boosted by early identification. Breast cancer screening is most commonly accomplished with mammography. The radiologist's experience and the image quality, among several other factors, might affect how well a mammogram is interpreted. Enhancing mammography analysis and increasing cancer early detection are made possible by medical image processing technology. Early identification is essential for improved treatment options and results for breast cancer, which is the primary cause of... see more
Emerging Biomedical Imaging Informatic for Smart Healthcare
Biomedical imaging informatics is a field that deals with the acquisition, processing, and interpretation of images in biomedicine using computational approaches. Biomedical imaging has progressed from early, simple X-rays for fracture diagnosis and foreign body identification to various techniques used to research biological structure and function and answer fundamental biomedical questions. New technologies for non-invasively interrogating whole 3-dimensional bodies have been developed via MRI (Magnetic Resonance Imaging), ultrasound, nuclear imaging, OI (Optical Imaging), CT (Computed... see more
Advancements in Deep Learning based Medical Image and Signal Processing for Healthcare Applications
The accurate predication from medical data (signals and images) analytics will be achieved through the use of cutting-edge AI tools for tailoring medical treatment to each patient. When the agents are done analysing the user's health data, the user can consult with the agents to receive advice and the most up-to-date information regarding their health (such as images, audio recordings, and biosignals). For many reasons, including the development of tailored treatment plans, the continual monitoring... see more