Advances in Biomedical Image Processing and Artificial Intelligence for Computer-Aided Diagnosis in Medicine

Dear Colleagues, 

With the digitization of medical data, various artificial intelligence techniques are now being employed. Radiomics and texture analysis are possible through the use of positron emission tomography, computerized tomography, and magnetic resonance imaging. Machine and deep learning techniques can aid in improving therapeutic tools, diagnostic decisions, and rehabilitation. Nevertheless, diagnosing patients has become more difficult due to the abundance of data from different imaging techniques, patient diversity, and the necessity to consider data from various sources, leading to the domain shift problem. Radiologists and pathologists rely on computer-aided diagnosis (CAD) systems to analyze biomedical images and address these challenges. CAD systems help reduce inter- and intra-observer variability, which occurs when different physicians assess the area under the same assumptions or at different times. Additionally, data access can be prevented due to privacy, security, and intellectual property concerns. Synthetic data are increasingly being explored in this context to address these issues. This Special Issue is connected to the 2nd International Workshop on Artificial Intelligence and Radiomics in Computer-Aided Diagnosis (AIRCAD 2023) but is open to additional submissions addressing topics relevant to the Special Issue’s scope. This Special Issue will cover the latest developments in biomedical image processing using machine learning, deep learning, artificial intelligence, and radiomics features, focusing on practical applications and their integration into the medical image processing workflow.

Potential topics include but are not limited to the following: biomedical image processing; machine and deep learning techniques for image analysis (i.e., the segmentation of cells, tissues, organs, and lesions and the classification of cells, diseases, tumors, etc.); image registration techniques; image preprocessing techniques; image-based 3D reconstruction; computer-aided detection and diagnosis systems (CADs); biomedical image analysis; radiomics and artificial intelligence for personalized medicine; machine and deep learning as tools to support medical diagnoses and decisions; image retrieval (e.g., context-based retrieval and lesion similarity); CAD architectures; advanced architectures for biomedical image remote processing, elaboration, and transmission; 3D vision, virtual, augmented, and mixed reality applications for remote surgery; image processing techniques for privacy-preserving AI in medicine.

Dr. Andrea Loddo
Dr. Albert Comelli
Dr. Cecilia Di Ruberto
Dr. Lorenzo Putzu
Dr. Alessandro Stefano
Guest Editors

Manuscript Submission Information

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