In case you missed the live, here the recording of the BI2021 Special Session on EXPLAINABLE ARTIFICIAL INTELLIGENCE FOR UNVEILING THE BRAIN: FROM THE BLACK-BOX TO THE GLASS-BOX (XAIB) 18th September 2021, 14:00-16:00 UK time (GMT+1), with Dr. Rich Caruana, Dr. Michele Ferrante and Dr. Dimitris Pinotsis:
Brain Hemispheric Specialization and its pathological change revealed by Neuroimaging and Neuropsychology
Brain hemispheric specialization has a fundamental role in the functional cortical organization in humans and the morphological, functional, metabolic and connectivity asymmetries of brain regions were traditionally correlated to the optimal information processing, language function, visuospatial task, attention, and many aspects of emotion.
The laterality of human brain varies with the aging and the variations from the normal pattern of asymmetry could be often suggestive of pathology. Indeed, it has been suggested that abnormal asymmetry may serve as a neuroanatomical marker or as a risk factor. In other words, the existence of asymmetry in brain regions where the symmetry is expected or, on the contrary, the absence of asymmetry where asymmetry is expected could be often indicative of neurological or neurodegenerative disorder.
Our aim is to investigate whether and how the brain asymmetries change, and the abnormalities could cause behavioral, cognitive, and symptomatic alterations in neurological diseases, explored through the new advanced technology in the field. The present Research Topic is to collect scientific works on the brain lateralization investigated with Neuroimaging approaches and Neuropsychological assessments in neurological disease. The following contributions are welcome:
• New research on biomarkers of brain lateralization; • Novel neuroimaging methods to characterize the brain asymmetry; • Novel neuropsychological assessments and techniques to better identify the brain specialization; • Correlation study between neuroimaging findings and neuropsychological evaluations; • Works exploring clinical conditions of asymmetry through multi-modal imaging approaches such as MRI, fMRI, DTI, PET or MEG; • Neuroimaging and Neuropsychological asymmetrical data investigations through Artificial Intelligence and Machine Learning; • Sex- and age-linked differences in brain asymmetry and neuropsychology patterns; • Longitudinal studies on the brain asymmetry progression in normal aging or diseases; • Identification of new metrics or indices for calculating the brain asymmetry; • Review articles that summarize the current literature on the brain hemispheres specialization.
A great virtual OHBM this year! I proposed the application of Interpretable Artificial Intelligence on Neuroimaging data through the Explainable Boosting Machine on Alzheimer’s data. Here my poster and a video presenting my work.
THE 14TH INTERNATIONAL CONFERENCE ON BRAIN INFORMATICS 2021
SPECIAL SESSION ON
EXPLAINABLE ARTIFICIAL INTELLIGENCE FOR UNVEILING THE BRAIN: FROM THE BLACK-BOX TO THE GLASS-BOX (XAIB)
+++++ CALL FOR PAPERS AND ABSTRACTS +++++
Nowadays, Artificial Intelligence (AI) and Machine Learning (ML) are widely used for the exploration of the Brain and their application ranges from the processing and analysis of neuroimages to the automatic diagnosis of the neurodegenerative diseases. However, without an explanation of the ML findings, the automatic medical and clinical decisions are still hard to be trusted. Indeed, the black-box nature of most algorithms, although providing high accuracy, makes the interpretation of the predictions not immediate. Thus, in recent years the need of interpretable and explainable AI, especially in Healthcare, got stronger, as well as the need of glass-box models able to show a trade-off between intelligibility and optimal performance.
The aim of this Special Session is to collect scientific works devoted to the new challenge of Explainable Artificial Intelligence applied on Neuroscience, Neuroimaging and Neuropsychological data for unveiling the Brain. Researchers are encouraged to submit high quality papers or abstracts on novel or state-of-the-art intelligible, interpretable, and understandable AI approaches, such as post-hoc explainability techniques both model-agnostic (e.g., lime, shap) and model-specific (e.g., CNN, SVM, Random Forests), and transparent models (i.e., linear/logistic regression, decision trees, GAM), with special attention to global and local explanations. Systematic reviews and meta-analyses are also welcome.
I’m glad to invite you to submit original scientific contributions for the Special Issue “Machine Learning in Healthcare and Biomedical Application” that I’ve organized for the Journal “Algorithms” MDPI. The updated deadline is the 31th March 2021.
Please, feel free to contact me for further information.