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Current Medical Imaging

ISSN: 1875-6603

Current Medical Imaging publishes frontier review articles, original research articles, case reports, drug clinical trial studies, and guest- edited thematic issues on all the latest advances on medical imaging dedicated to clinical research. All relevant areas are covered by the journal, including advances in the diagnosis, instrumentation, and therapeutic applications related to all modern medical imaging techniques including but not limited to:

  • 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
The journal is essential reading for all clinicians and researchers involved in medical imaging and diagnosis.

Special Issues With Active Call for Papers

Submission closes on: Sep 03, 2024
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

Submission closes on: Aug 01, 2024
Intelligent Information Retrieval for Multispectral and Hyperspectral Imaging

Multispectral and hyperspectral imaging is an interdisciplinary research area, widely focuses on spectral, spatial, and temporal data. It offers a plethora of opportunities to efficiently analyze the vast area of the earth's surface. However, there are numerous factors such as dimensionality and size of the hyperspectral data, lesser training samples, scattering of light during data acquisition processes, varied pixels, and various geometric and atmospheric distortions makes the data to be inherently complex and challenging to... see more