New Sights of Deep Learning and Biomedical Image Processing: Updates, Applications, and Directions

Dear Colleagues,

Deep learning offers new opportunities to enhance the accuracy and efficiency of medical imaging data analysis. It has demonstrated remarkable progress in various tasks, including image segmentation, object detection, image reconstruction, and analysis across a wide range of multimodal images, such as CT, MRI, X-rays, ultrasound, optical imaging, and endoscope/laparoscope. These advancements have paved the way for developing more reliable and intelligent tools and devices for diagnostics and treatments.

However, several challenges remain that need to be addressed to fully realize the potential of deep learning in medical imaging. Issues such as model interpretability, adaptability to different domains, generalizability, and accuracy in real-world datasets, as well as ethical considerations, must be addressed to ensure that they complement existing workflows and enhance clinical outcomes.

This Special Issue of Bioengineering aims to highlight the latest advancements, innovative methodologies, and future directions in deep learning and image processing, particularly in their applications for biomedical imaging. We invite contributions that address these challenges and showcase novel solutions, cutting-edge research, and practical implementations that can bridge the gap between technological innovation and clinical practice.

We are pleased to invite submissions that address, but are not limited to, the following topics:

• Novel deep learning algorithms for biomedical image analysis;
• Applications of deep learning in medical diagnostics;
• Interpretability of deep learning algorithms for medical diagnostics;
• Integration and performance of AI tools in clinical practice;
• Specific challenges and future prospects in biomedical image processing;
• Perspective reviews on the current status of deep learning in medical imaging;
• Application of large vision models in the biomedical field;
• Data privacy and ethical considerations in AI-driven medical imaging;
• Real-time processing and edge computing for biomedical images;
• Multimodal data integration and analysis in biomedical imaging;
• Benchmarking and validation of AI models for clinical deployment;
• AI-driven personalized medicine and treatment planning.

Dr. Wenjun Wu
Dr. Lin Shan
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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.

• deep learning
• biomedical imaging
• optical coherence tomography
• microscopy, X-ray, CT, MRI, ultrasound
• endoscopy, laparoscopy, cystoscopy
• image classification
• image segmentation
• biomarker detection
• image registration
• multimodal medical imaging