Technological progress in the areas of artificial intelligence, specifically in the field of Deep Learning and Neuro-inspired networks, allows today to obtain results that would once have been considered almost impossible. Specifically, the modern approaches of quantitative analysis of medical images (CT-scans, MRI, PET, SPECT, etc..) and biomedical signals (Photoplethysmography(PPG), Electrocardiography(ECG), ElectroEncephaloGraphy(EEG), etc..) allow to extract a considerable amount of data to be used for the implementation of numerous bio-engineering applications, ranging from the development of recent Radiomics algorithms for applications in the field of oncology, cardiology or neurology, to the definition of bio-inspired algorithms based on the study and analysis of physiological signals of human subjects for applications in the automotive sector such as ADAS systems, Robotic systems for industrial and medical applications, autonomous driving, etc.
The objective of this Research Topic is to collect scientific contributions that confirm the advantages inherent in the application of modern techniques of Deep Learning and Bio-inspired Neural algorithms in the analysis of medical images and biomedical signals for providing bio-engineering applications in different research areas with special focus in the robotic, medical and industrial fields. More in details, the target of this Research Topic is:
a) to provide a comprehensive overview of the most recent advanced bio-inspired neural pipelines for improving/proposing new and efficient solutions for particularly complex problems in the robotic, medical and industrial fields or ;
b) to investigate multimodal analysis of biomedical data, signals and images for medical, robotic and industrial applications.
The expected topics include, but are not limited to the following:
• Bio-inspired systems for Radiomics/Pathomics algorithms in medical oncology;
• Cancer dynamic mathematical modeling based on medical images/physiological signals;
• Motion magnification in medical imaging and bio-signals analysis;
• Fractal, chaos and complexity in medical images and biomedical signals;
• Neuro-inspired algorithms for Biomedical signals processing in medical field;
• Recent advances of Deep Learning in medical imaging for oncological applications;
• Recent advances of Deep Learning in medical imaging for neurological applications;
• Bio-Inspired Neural Systems for chemotherapeutic outcome prediction from medical image analysis (CT-Scans, MRI, SPECT, PET, Echo);
• Bio-Inspired Neural Systems for anti PD-1/PD-L1 immunotherapeutic outcome prediction from medical image analysis (CT-Scans, MRI, SPECT, PET, Echo);
• Bio-Inspired Pipelines for Biomedical signals and medical images data fusion for medical applications;
• Bio-Inspired Radiomics Pipelines and Deep Learning for Cancer early diagnosis;
• Neuro-Inspired Bio-signals and Medical Image Processing Pipelines for Industrial and healthcare applications;
• Bio-Inspired Physiological bio-signals (PPG, ECG, EEG, etc..) processing pipelines for automotive/ADAS applications;
• Bio-Inspired Physiological bio-signals processing pipelines for industrial applications (Security, Profiling, Forensic applications, etc..);
• Bio-Inspired Physiological bio-signals processing pipelines for early diagnosis of cardiological pathologies;
Reviews and surveys of the state-of-the-art are also welcomed.
Note:
New articles will be added to this collection as they are published.
Manuscript submission will be possible to Frontiers in Computer Science, Frontiers in Neurorobotics, Frontiers in Neuroinformatics, Frontiers in Medicine and Frontiers in Bioengineering and Biotechnology. The final list of participating journals will be available on the website shortly. If you have any questions please contact the Editorial Office at computerscience@frontiersin.org
Disclosure: Topic Editor Dr. Francesco Rundo is employed by company STMicroelectronics - ADG, Central R&D Division. All other Topic Editors declare no competing interests with regards to the Research Topic subject.
Technological progress in the areas of artificial intelligence, specifically in the field of Deep Learning and Neuro-inspired networks, allows today to obtain results that would once have been considered almost impossible. Specifically, the modern approaches of quantitative analysis of medical images (CT-scans, MRI, PET, SPECT, etc..) and biomedical signals (Photoplethysmography(PPG), Electrocardiography(ECG), ElectroEncephaloGraphy(EEG), etc..) allow to extract a considerable amount of data to be used for the implementation of numerous bio-engineering applications, ranging from the development of recent Radiomics algorithms for applications in the field of oncology, cardiology or neurology, to the definition of bio-inspired algorithms based on the study and analysis of physiological signals of human subjects for applications in the automotive sector such as ADAS systems, Robotic systems for industrial and medical applications, autonomous driving, etc.
The objective of this Research Topic is to collect scientific contributions that confirm the advantages inherent in the application of modern techniques of Deep Learning and Bio-inspired Neural algorithms in the analysis of medical images and biomedical signals for providing bio-engineering applications in different research areas with special focus in the robotic, medical and industrial fields. More in details, the target of this Research Topic is:
a) to provide a comprehensive overview of the most recent advanced bio-inspired neural pipelines for improving/proposing new and efficient solutions for particularly complex problems in the robotic, medical and industrial fields or ;
b) to investigate multimodal analysis of biomedical data, signals and images for medical, robotic and industrial applications.
The expected topics include, but are not limited to the following:
• Bio-inspired systems for Radiomics/Pathomics algorithms in medical oncology;
• Cancer dynamic mathematical modeling based on medical images/physiological signals;
• Motion magnification in medical imaging and bio-signals analysis;
• Fractal, chaos and complexity in medical images and biomedical signals;
• Neuro-inspired algorithms for Biomedical signals processing in medical field;
• Recent advances of Deep Learning in medical imaging for oncological applications;
• Recent advances of Deep Learning in medical imaging for neurological applications;
• Bio-Inspired Neural Systems for chemotherapeutic outcome prediction from medical image analysis (CT-Scans, MRI, SPECT, PET, Echo);
• Bio-Inspired Neural Systems for anti PD-1/PD-L1 immunotherapeutic outcome prediction from medical image analysis (CT-Scans, MRI, SPECT, PET, Echo);
• Bio-Inspired Pipelines for Biomedical signals and medical images data fusion for medical applications;
• Bio-Inspired Radiomics Pipelines and Deep Learning for Cancer early diagnosis;
• Neuro-Inspired Bio-signals and Medical Image Processing Pipelines for Industrial and healthcare applications;
• Bio-Inspired Physiological bio-signals (PPG, ECG, EEG, etc..) processing pipelines for automotive/ADAS applications;
• Bio-Inspired Physiological bio-signals processing pipelines for industrial applications (Security, Profiling, Forensic applications, etc..);
• Bio-Inspired Physiological bio-signals processing pipelines for early diagnosis of cardiological pathologies;
Reviews and surveys of the state-of-the-art are also welcomed.
Note:
New articles will be added to this collection as they are published.
Manuscript submission will be possible to Frontiers in Computer Science, Frontiers in Neurorobotics, Frontiers in Neuroinformatics, Frontiers in Medicine and Frontiers in Bioengineering and Biotechnology. The final list of participating journals will be available on the website shortly. If you have any questions please contact the Editorial Office at computerscience@frontiersin.org
Disclosure: Topic Editor Dr. Francesco Rundo is employed by company STMicroelectronics - ADG, Central R&D Division. All other Topic Editors declare no competing interests with regards to the Research Topic subject.
