CASUS Institute Seminar, Adrian Urbański, master student at the University of Wrocław, Scultetus fellow at CASUS
Abstract of the talk// Advancements in biomedical imaging and supervised machine learning (ML) carry the potential to revolutionize clinical diagnostics. However, the application of supervised ML techniques requires extensive datasets annotated by specialized biomedical experts – a laborious and challenging endeavor, particularly in highly specialized cases like clinical urine microscopy. Here, an evaluation of methodologies well-established in data science including self-supervised learning, teacher-student learning, and weak labeling employing a clinical dataset is presented. In a systematic comparison, an optimal approach for leveraging extensive quantities of unlabelled data is formulated, facilitating the acquisition of enhanced representations. The results of this work hold the promise of improving downstream predictions.
Adrian will be talking live in Görlitz. However, as the event is organized in a hybrid format that includes a videoconferencing tool by Zoom Inc., people interested in the topic have the chance to also join the talk remotely. Please ask for the login details via firstname.lastname@example.org