Diagnostic Biomedical Image and Processing with Artificial Intelligence and Deep Learning

Dear Colleagues,

As technology continues to evolve, biomedical imaging, including radiographic images (CT, MRI, PET, SPECT, etc.), pathological images, ophthalmic images (Optical Coherence Tomography - OCT, OCT Angiography - OCTA, fundus photography, and fluorescein angiography), microscopy imaging, protein images, and other related biomedical images, are playing an increasingly important role in assisting clinical disease diagnosis, treatment decisions, and scientific research. With the continuous enrichment of large-scale image datasets and the ongoing advancement of cutting-edge parallel graphics processing units, advanced image processing techniques, particularly the integration of biomedical imaging and AI, hold the potential to further enhance diagnostic efficiency and accuracy. This progress is expected to significantly advance scientific research in the field of biomedical imaging.

Advanced image processing techniques, particularly AI-based methods represented by deep learning, have been widely applied to various tasks in biomedical images. These tasks range from image classification, image segmentation, image reconstruction, image super-resolution, image registration, and image fusion to disease classification, lesion detection, and survival prediction. However, the challenges of AI in biomedical image analysis still require further resolution.

We are pleased to invite you as contributors of this Special Issue to publish your experimental and theoretical results of new approaches and applications in biomedical imaging. This Special Issue aims to focus on articles and cutting-edge technology reviews that apply the most advanced techniques to

biomedical image processing and applications. The topics include constructing data-efficient deep learning models to address the demands of large datasets, establishing models with efficient data annotation, enhancing algorithm robustness and interpretability to create high-confidence models, and developing more efficient and advanced algorithms for specific tasks.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but not limited to) the following:

1. Advanced image processing techniques applied to biomedical imaging:

Image segmentation, image reconstruction, image super-resolution, image registration, image fusion.

2. Advanced application technologies based on biomedical imaging:

Image and disease classification, object and lesion detection, organ region and marker localization, organ and structure segmentation, survival prediction, radiation therapy planning, assistive treatment, surgical navigation, innovative approaches in large model techniques, fusion of biomedical imaging and multimodal information.

3. Data-efficient models based on biomedical imaging:

Training methods based on limited annotated data (unsupervised learning, semi-supervised learning, self-supervised learning, and weakly supervised learning), efficient domain adaptation models and approaches, efficient data annotation models and approaches.

4. Applications of novel imaging and imaging techniques in biomedical and engineering fields:

Cutting-edge imaging techniques such as super-resolution imaging, fast image reconstruction and imaging techniques, emerging radiographic imaging, latest imaging techniques for pathological images, microscopic images, and the integration of virtual reality and augmented reality technologies with ai in biomedical imaging. Also, applications in the biomedical field utilizing novel imaging combined with AI, including the use of protein imaging and digital signal images.

5. Other relevant technical articles and state-of-the-art technology reviews in the field.

We look forward to receiving your contributions.

Prof. Dr. Zhijian Song
Prof. Dr. Manning Wang
Dr. Shuo Wang
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Bioengineering is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• biomedical image
• artificial intelligence
• deep learning
• image processing
• medical imaging
• computer-assisted diagnosis
• pattern recognition
• computer vision
• bioengineering