AI in Computational Bioimaging

The intersection of artificial intelligence (AI) and computational bioimaging represents a transformative frontier in research and clinical practice. Over recent decades, advancements in imaging technologies such as MRI, CT, PET, and confocal microscopy have generated vast amounts of biological and medical data. However, the sheer scale and complexity of this data make it challenging for traditional analysis methods to fully exploit the information contained within. AI, particularly deep learning and machine learning algorithms, offers powerful tools for automating and enhancing image analysis, leading to more accurate and efficient diagnostic and prognostic procedures.

AI techniques in computational bioimaging have shown promise in diverse applications, from automating the detection and classification of cellular structures and pathological features to enhancing image quality and resolution. Despite significant strides, the integration of AI in bioimaging is not without challenges, including issues related to data standardization, interpretability of AI models, and the need for extensive, annotated datasets for algorithm training.

The aim of this research topic is to explore cutting-edge applications and innovations of AI in the field of computational bioimaging. We seek to bring together researchers and practitioners to discuss breakthroughs, address current challenges, and propose future directions in leveraging AI technologies to enhance biological and medical image analysis and interpretation.

We invite a wide range of submissions that contribute to the understanding and development of AI in computational bioimaging. We welcome original research articles, reviews, case studies, technology and code, methodologies, and perspective that might explore but are not limited to:

1. AI Algorithms for Image Analysis: Submissions exploring novel AI algorithms specifically designed for the analysis and interpretation of bioimaging data. This includes segmentation, classification, and detection tasks.
2. AI Integration of Multi-modal Imaging Data: Research focused on methodologies for integrating and analyzing data from multiple imaging modalities to improve diagnostic accuracy and understanding of complex biological processes.
3. AI-driven Biomarker Discovery: Papers highlighting the use of AI in identifying and validating imaging biomarkers that could enhance disease prognosis and treatment strategies.
4. Improving Imaging Resolution and Quality with AI: Studies on AI techniques that enhance the quality, resolution, and utility of bioimaging data, including denoising and super-resolution models.
5. Interpretability and Explainability of AI Models: Research addressing the transparency and understanding of AI systems applied to bioimaging, crucial for clinical acceptance and reliability.
6. Ethical and Regulatory Considerations: Insights into the ethical implications and necessary regulatory frameworks for the clinical implementation of AI technologies in bioimaging.

By covering a broad spectrum of topics, this collection aims to provide a comprehensive insight into how AI is reshaping the landscape of bioimaging.

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
• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• General Commentary
• Hypothesis and Theory
• Methods
• ... View all formats

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
• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Review
• Systematic Review
• Technology and Code

Keywords: Artificial Intelligence (AI), Computational Bioimaging, Deep Learning, Image Segmentation, Biomarker Discovery, Multi-modal Imaging, Model Interpretability, Clinical Diagnostics

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.