Journal of Medical Imaging

Aims & Scope

JMI covers fundamental and translational research, as well as applications, focused on medical imaging, which continue to yield physical and biomedical advancements in the early detection, diagnostics, and therapy of disease as well as in the understanding of normal.

The scope of JMI includes: Imaging physics, Tomographic reconstruction algorithms (such as those in CT and MRI), Image processing and deep learning, Computer-aided diagnosis and quantitative image analysis, Visualization and modeling, Picture archiving and communications systems (PACS), Image perception and observer performance, Technology assessment, Ultrasonic imaging, Image-guided procedures, Digital pathology, Biomedical applications of biomedical imaging.

JMI allows for the peer-reviewed communication and archiving of scientific developments, translational and clinical applications, reviews, and recommendations for the field.

View Aims & Scope

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Metrics & Ranking

Impact Factor

Year	Value
2025	1.7
2024	1.90

SJR (SCImago Journal Rank)

Year	Value
2024	0.523

Quartile

Year	Value
2024	Q2

h-index

Year	Value
2024	54

Journal Rank

Year	Value
2024	11107

Journal Citation Indicator

Year	Value
2024	823

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Abstracting & Indexing

Journal is indexed in leading academic databases, ensuring global visibility and accessibility of our peer-reviewed research.

• Scopus
• Google Scholar
• Web of Science

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Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in Medicine, designed to support cutting-edge academic discovery.

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Most Cited Articles

The Most Cited Articles section features the journal's most impactful research, based on citation counts. These articles have been referenced frequently by other researchers, indicating their significant contribution to their respective fields.

• Recurrent residual U-Net for medical image segmentation

  Citation: 647

  Authors: Md Zahangir, Chris, Mahmudul, Tarek M., Vijayan K.

• Digital mammographic tumor classification using transfer learning from deep convolutional neural networks

  Citation: 414

  Authors: Benjamin Q., Hui, Maryellen L.

• DeepLesion: automated mining of large-scale lesion annotations and universal lesion detection with deep learning

  Citation: 388

  Authors: Ke, Xiaosong, Le, Ronald M.

• Global image registration using a symmetric block-matching approach

  Citation: 287

  Authors: Marc, David M., Pankaj, Gavin P., John S., Sébastien

• Mitosis detection in breast cancer pathology images by combining handcrafted and convolutional neural network features

  Citation: 279

  Authors: Haibo, Angel, Ajay, Hannah, Natalie, Mike, John, Fabio, Anant

• History and future technical innovation in positron emission tomography

  Citation: 219

  Authors: Terry, David

• Fully automated quantitative cephalometry using convolutional neural networks

  Citation: 218

  Authors: Sercan Ö., Bulat, Lei

• Family of boundary overlap metrics for the evaluation of medical image segmentation

  Citation: 159

  Authors: Varduhi, Irina

• MARIA M4: clinical evaluation of a prototype ultrawideband radar scanner for breast cancer detection

  Citation: 153

  Authors: Alan W., Ian, Mike, Lyn, Helen L.

• Virtual clinical trials in medical imaging: a review

  Citation: 153

  Authors: Ehsan, William P., Benjamin M. W., Paul E., Nick, Alejandro F., Andrew, Joseph, Ehsan

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