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BMC Medical Imaging

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Featured article: MIScnn: a framework for medical image segmentation with convolutional neural networks and deep learning

The increased availability and usage of modern medical imaging induced a strong need for automatic medical image segmentation. In this Software article, Müller & Kramer introduce the open-source Python library MIScnn. The framework enables researchers to rapidly set up a complete medical image segmentation pipeline by using just a few lines of code.

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Featured article: COVID-19 lung CT image segmentation using deep learning methods: U-Net versus SegNet

Saood and Hatem investigate two structurally-different deep learning techniques, SegNet and U-NET, for semantically segmenting COVID-19 infected tissue regions in CT lung images. They propose that the computer-based techniques may prove as reliable detectors for infected tissue in lung CT scans.

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Featured article: Universal adversarial attacks on deep neural networks for medical image classification

Hirano et al focus on three representative deep neural network-based medical image classification tasks and investigate their vulnerability to the seven model architectures of universal adversarial perturbations.

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Aims and scope

BMC Medical Imaging is an open access journal publishing original peer-reviewed research articles in the development, evaluation, and use of imaging techniques and image processing tools to diagnose and manage disease.

Peer Review Taxonomy

This journal is participating in a pilot of NISO/STM's Working Group on Peer Review Taxonomy, to identify and standardize definitions and terminology in peer review practices in order to make the peer review process for articles and journals more transparent. Further information on the pilot is available here.

The following summary describes the peer review process for this journal:

  • Identity transparency: Single anonymized
  • Reviewer interacts with: Editor
  • Review information published: Review reports. Reviewer Identities reviewer opt in. Author/reviewer communication

We welcome your feedback on this Peer Review Taxonomy Pilot. Please can you take the time to complete this short survey.

Call for Content: AI in Medical Imaging

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© metamorworks

BMC Medical Imaging welcomes submissions to this new collection focusing on deepening the understanding of artificial intelligence in medical imaging, highlighting its versatility and applications, and breaking down barriers that still exist in the field. 
Find more information and explore recent publications here.

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Editor

Piero Lo Monaco, BMC series, Germany

Assistant Editor

Kate Gaines, BMC series, USA

Get credit for your data!

New Content Item © © ra2studio

Valuable data often go unpublished when they could be helping to progress science. Hence, the BMC Series introduced Data notes, a short article type allowing you to describe your data and publish them to make your data easier to find, cite and share.

You can publish your data in BMC Genomic Data (genomic, transcriptomic and high-throughput genotype data) or in BMC Research Notes (data from across all natural and clinical sciences). 

More information about our unique article type can be found on the BMC Genomic Data and BMC Research Notes journal websites. 

BMC Series Focus Issues

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January: Antimicrobial resistance

Microbial resistance has become a major threat to human health in the 21st century. In order to provide the public with a general understanding of the situation of drug-resistant pathogens, and to alert and prevent this threat, the BMC Series Journals dedicate their first focus issue in 2022 to research on antimicrobial resistance.

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December: HIV

For World AIDS day, the BMC Series Journals dedicate this month's focus issue to research on HIV and AIDS highlighting recent advancements in understanding the biology of the disease, the continued breakthroughs in diagnosis and treatment and also shed some light on what life with HIV is like. 

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BMC Medical Imaging

ISSN: 1471-2342

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