Due to the power of computing and the proliferation of biomedical imaging modalities such as Photoacoustic Tomography and Computed Tomography (CT), Artificial Intelligence has been increasingly used in biomedical image analysis. Over the past few decades, we have witnessed the great success of AI applied in all kinds of biomedical imaging, including X-ray, ultrasound, computerized tomography (CT), MRI, fMRI, positron emission tomography (PET), and single-photon emission computed tomography (SPECT).

Among the most promising biomedical applications of AI is diagnostic imaging, and increasing attention is being directed at establishing and fine-tuning its performance to facilitate detection and quantification of a wide array of clinical conditions. Meanwhile, imaging researchers are also faced with challenges in data management, indexing, query, and analysis of digital pathology data. One of the main challenges is how to manage relatively large-scale, multi-dimensional data sets that will continue to expand over time since it is unreasonable to exhaustively compare the query data with each sample in a high-dimensional database due to practical storage and computational bottlenecks. The second challenge is how to reliably interrogate the characteristics of data originating from multiple modalities.

This Special Issue aims to provide a diverse but complementary set of contributions to demonstrate new developments and applications that cover the above issues in the application of AI for biomedical image analysis. We would also like to accept successful applications of the new methods, including but not limited to data processing, analysis, management, and knowledge discovery of biomedical data.

Potential topics include but are not limited to the following:

• Feature extraction by deep learning or sparse codes for biomedical data
• Data representation of biomedical data
• Dimensionality reduction techniques (subspace learning, feature selection, sparse screening, feature screening, feature merging, etc) for biomedical data
• Information retrieval for biomedical data
• Kernel-based learning for multi-source biomedical data
• Incremental learning or online learning for biomedical data
• Data fusion for multi-source biomedical data
• Missing data imputation for multi-source biomedical data
• Data management and mining in biomedical data
• Web search and meta-search for biomedical data
• Web information retrieval for biomedical data
• Biomedical data quality assessment
• Transfer learning of biomedical data