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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
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
Machine Learning Algorithms for Early Detection of Cancer in Medical Imaging
Computers may learn from training data using machine learning (ML), a subset of artificial intelligence. ML has shown great predictive power for a number of cancers, including brain, liver, prostate, breast, and lung cancers. Indeed, studies have shown that AI and ML can predict cancer more accurately than doctors. Newly developed medical technologies that employ liquid biopsies to detect cancer early might provide an alternative to established cancer screening initiatives. These tests have the potential... 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
Multimodal Imaging Using Spectroscopic Techniques for Improved Surgical Guidance
The majority of patients with solid malignancies have surgical excision as the primary and most successful method of medical care. On the other hand, patients experience recurrence and subsequent recurrence. The objective of this thematic issue is to provide a simpler and more concise explanation of the obstacles faced by physicians and the ways in which vibrational spectroscopy has developed to address those needs that have the greatest potential for advancement in terms of tissue... 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
Quantum Machine Learning for Medical Data and Imaging
Quantum computing has promised a significant speedup in certain computationally intensive tasks that are intractable on classical computers. Researchers in quantum machine learning, such as those working in computer vision, image processing, biomedical analysis, and related topics, may play an important role in comprehending and working on complicated medical data, which ultimately improves patient care when paired with skilled clinicians. Creating a new quantum machine-learning algorithm tailored to biomedical data is difficult and urgent. There... 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