Deep Learning for Medical Imaging Applications

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About this Research Topic

Submission deadlines

  1. Manuscript Submission Deadline 31 December 2024

  2. This Research Topic is still accepting articles.

    1. Check author guidelines

Background

Artificial Intelligence (AI) is revolutionizing the dynamics of technological advancement in the field of medical imaging, distinctly contributing to a significant paradigm shift in healthcare. AI serves as a powerful tool for efficiently processing massive datasets of medical images, thereby deciphering disease characteristics invisible to human perception. AI-powered advances are instrumental in enhancing diagnostic accuracy, fortifying data processing capabilities, and consequently, crafting a more personalized patient treatment approach. However, despite such advancements, the technology has yet to fully answer several queries concerning the effectiveness, dependability, scalability, transparency, and interpretability of AI algorithms. Research targeted at alleviating these uncertainties has brought forth promising avenues. One notable breakthrough is the ability of AI models to accurately discern anomalies in health conditions, which have historically posed significant challenges even for experienced medical professionals.

Despite AI’s promising trajectory in medical imaging, current challenges related to data quality, privacy, interpretability, and regulatory considerations persist in impeding its broad adoption. As such, the primary objective of this research topic is to establish a collaborative platform for imaging researchers, fostering the sharing of knowledge and experiences at the intersection of AI and medical imaging technologies. We encourage research submissions that delve into a thorough examination of AI’s fundamental components, critically evaluate performance metrics, and highlight the practical implications of AI algorithms in the scope of medical imaging. A critical area of focus includes addressing challenges associated with augmenting the interpretability of AI algorithms and making them accessible to healthcare professionals devoid of a computational background. Furthermore, identifying potential sources of bias within AI models and devising strategies to mitigate these biases is paramount. This research ultimately aims to investigate strategies that facilitate the creation of universally adaptable, resilient AI models that can excel across varied healthcare environments, irrespective of resource availability.

To gather further insights into the practical applications and limitations of artificial intelligence methods in medical imaging, we welcome submissions of articles that explore but are not limited to, the following themes:

• Advancements in deep learning techniques applied across diverse imaging modalities, including X-ray Imaging, Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Ultrasound Imaging, Positron Emission Tomography (PET), Fluoroscopy, and natural images.
• Crafting and training AI models for interpreting massive medical image datasets.
• Addressing AI bias and proposing strategies for training with limited datasets
• Developing AI models capable of analyzing heterogeneous datasets, composed of images derived from multiple modalities and instruments.
• Making AI models more explainable and user-friendly for healthcare professionals, thereby promoting their widespread adoption.
• Generative models in medical imaging.
• Addressing the implications of AI in healthcare privacy and ethical issues.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

  • Brief Research Report
  • Case Report
  • Clinical Trial
  • Community Case Study
  • Conceptual Analysis
  • Data Report
  • Editorial
  • Hypothesis and Theory
  • Methods

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

  • Brief Research Report
  • Case Report
  • Clinical Trial
  • Community Case Study
  • Conceptual Analysis
  • Data Report
  • Editorial
  • Hypothesis and Theory
  • Methods
  • Mini Review
  • Opinion
  • Original Research
  • Perspective
  • Review
  • Systematic Review
  • Technology and Code

Keywords: Artificial Intelligence, Explainable AI, Medical Data Generation, AI Diagnostic Tools, Machine Learning, Healthcare AI, Data Annotation, Computational Medicine, PET, CT, FUS, Endoscopy, Radiomics, Neural Networks (CNNS), Computer-Aided Diagnosis (CAD), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), Medical Imaging and Analysis, MRI

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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Manuscripts can be submitted to this Research Topic via the main journal or any other participating journal.

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