Скачать с ютуб End-to-End Design of Deep Learning for Computational Pathology | Mahdi S. Hosseini, PhD в хорошем качестве

End-to-End Design of Deep Learning for Computational Pathology | Mahdi S. Hosseini, PhD

The computational advantages of deep learning in AI, integrated with digital pathology for microscopy imaging, has led to the emergence of a new field called Computational Pathology (CoPath) that is poised to transform clinical pathology globally. The field of CoPath is dedicated to the creation of automated tools that address and aid steps in the clinical workflow for cancer diagnostics. With increasing advancements in deep learning, image analytics, and enabling hardware, the research focus in this field has expanded and branched into a broad range of domains. In this seminar we present our theoretical advancements in deep learning and computer vision algorithms with focused application in CoPath. We investigate this from both data-centric and model-centric approaches to cohesively relate between “data” and “learning-models” so to effectively design, train, and rationalize our algorithmic decisions. PERFORM Centre: https://www.concordia.ca/perform