Advancements in Machine Learning for Neuroimaging Analysis скачать в хорошем качестве

Advancements in Machine Learning for Neuroimaging Analysis

Dr. Alex Crimi (Brain and More Lab, Center for Computational Medicine, Krakow, Poland) talks about Advancements in Machine Learning for Neuroimaging Analysis: Bridging the Gap between Data and Diagnosis Abstract: Medical imaging, particularly neuroimaging, has experienced a paradigm shift with the integration of machine learning techniques. This talk delves into the transformative role of machine learning in analyzing neuroimaging data, with a focus on its applications and implications in clinical practice. The advent of machine learning algorithms has enabled the extraction of intricate patterns from complex neuroimaging datasets, providing insights into neurological disorders with unprecedented precision. Through the amalgamation of advanced imaging modalities such as magnetic resonance imaging (MRI), and positron emission tomography (PET) coupled with machine learning models, researchers have achieved remarkable strides in disease diagnosis, prognosis, and treatment response prediction. This presentation will elucidate various machine learning approaches employed in neuroimaging analysis, including but not limited to convolutional neural networks (CNNs), recurrent neural networks (RNNs), and graph-based methods. These methodologies empower the automatic segmentation, classification, and feature extraction of brain structures and abnormalities, facilitating early detection and characterization of neurological conditions such as Alzheimer's disease, Parkinson's disease, multiple sclerosis, and brain tumors. Lastly, we will discuss potential translation from science into startup for students.