Journal of Medical Imaging

Editor-in-Chief: Maryellen L. Giger, The University of Chicago, USA

The Journal of Medical Imaging allows for the peer-reviewed communication and archiving of fundamental and translational research, as well as applications, focused on medical imaging, a field that continues to benefit from technological improvements and yield biomedical advancements in the early detection, diagnostics, and therapy of disease as well as in the understanding of normal conditions.

On the cover: The figure is from the paper "Radio-pathomic mapping model generated using annotations from five pathologists reliably distinguishes high-grade prostate cancer" by Sean D. McGarry et al.

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Validation of two techniques for intraoperative hyperspectral human tissue determination

Eric L. Wisotzky et al.

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Hippocampus segmentation in CT using deep learning: impact of MR versus CT-based training contours

Annika Hänsch et al.

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Spatiotemporal feature extraction and classification of Alzheimer’s disease using deep learning 3D-CNN for fMRI data

Harshit Parmar et al.

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Validation of two techniques for intraoperative hyperspectral human tissue determination

Hippocampus segmentation in CT using deep learning: impact of MR versus CT-based training contours

Spatiotemporal feature extraction and classification of Alzheimer’s disease using deep learning 3D-CNN for fMRI data

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

Deep neural network to locate and segment brain tumors outperformed the expert technicians who created the training data

Virtual clinical trials in medical imaging: a review

Spatiotemporal feature extraction and classification of Alzheimer’s disease using deep learning 3D-CNN for fMRI data

Silicon photon-counting detector for full-field CT using an ASIC with adjustable shaping time

Recurrent residual U-Net for medical image segmentation

Algorithm to quantify nuclear features and confidence intervals for classification of oral neoplasia from high-resolution optical images

Radio-pathomic mapping model generated using annotations from five pathologists reliably distinguishes high-grade prostate cancer

Automatic mass detection in mammograms using deep convolutional neural networks

Integrating AI into radiology workflow: levels of research, production, and feedback maturity

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