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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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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Automatic multi-organ segmentation in computed tomography images using hierarchical convolutional neural network

Sharmin Sultana et al.

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

Joseph Ross Mitchell et al.

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

Automatic multi-organ segmentation in computed tomography images using hierarchical convolutional neural network

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

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Water–fat magnetic resonance imaging quantifies relative proportions of brown and white adipose tissues: ex-vivo experiments

Preoperative measurement of cutaneous melanoma and nevi thickness with photoacoustic imaging

Statistical atlas-based descriptor for an early detection of optic disc abnormalities

Combination of computer extracted shape and texture features enables discrimination of granulomas from adenocarcinoma on chest computed tomography

Optimized b-value selection for the discrimination of prostate cancer grades, including the cribriform pattern, using diffusion weighted imaging

Predicting malignant nodules by fusing deep features with classical radiomics features

Dynamic contrast-enhanced magnetic resonance imaging and diffusion-weighted magnetic resonance imaging for predicting the response of locally advanced breast cancer to neoadjuvant therapy: a meta-analysis

Cancer imaging phenomics toolkit: quantitative imaging analytics for precision diagnostics and predictive modeling of clinical outcome

GIFTed Demons: deformable image registration with local structure-preserving regularization using supervoxels for liver applications

Probabilistic visual and electromagnetic data fusion for robust drift-free sequential mosaicking: application to fetoscopy

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