Advanced Deep Learning for Biomedical Sensing and Imaging

Dear Colleagues,

The emergence of deep learning (DL) has sparked revolutionary transformations in a broad spectrum of biomedical imaging techniques by offering a distinctive data-driven approach. Within only a few years, DL has achieved great success and significantly improved imaging performance beyond instrumental limitations for various imaging modalities, such as fluorescence imaging, fluorescence lifetime imaging, super resolution imaging, optical coherence tomography, Fourier ptychographics, and scattering media imaging, among others. DL has also pioneered novel functionalities for enhanced image interpretation, such as image classification/segmentation and cross-modality image transformation. These impressive achievements have empowered researchers to gain deeper insights into biophysical phenomena and develop potent tools for computer-aided diagnosis and surgical guidance.

This Special Issue aims to assemble recent research from biomedical imaging and sensing communities regarding DL applications, innovative biomedical imaging techniques and their novel applications. The scope of this Special Issue encompasses, but is not limited to, the following topics:

• Deep learning algorithms for biomedical image and signal analysis, including fluorescence sensing and imaging, fluorescence lifetime sensing and imaging, optical coherence tomography, diffuse tomography, and endoscopy;
• Deep learning for data analysis in biomedical sensors;
• Biomedical image reconstruction, denoise, and resolution enhancement based on sensors;
• Biomedical image and sensor signal classification and segmentation;
• Biomedical imaging-assisted clinical diagnosis and surgical guidance;
• Multi-modality image transformation based on sensors;
• Object detection and localization based on sensors;
• On device deep learning for biomedical sensing and imaging.

Dr. Dong Xiao
Dr. Yahui Li
Guest Editors

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