AI in Medicine and Science
Join us for a wide range of exciting talks about topics in the (medical) machine learning domain!
Upcoming Talks
Causal discovery aims to learn causal networks, i.e., directed acyclic graphs (DAGs), from observational data. Although the problem is not feasible in the most general form, as it is not possible to infer causal relations from pure correlations, we can define structural assumptions about the functional relations underlying our observed system to ease this task.
Sep 16, 2024
In my presentation I will share the experience of the Institute of Pathology (University Hospital Cologne) concerning development of clinical-grade AI tools for pathology diagnostics, especially in the domain of malignant tumors.
Sep 2, 2024
Past Talks
Join the talk at: https://us04web.zoom.us/j/77917991926?pwd=DVVp8yRWGKuXH7cRubIesPYTccprE9.1 Self-supervised learning (SSL) is a strategy for addressing the paucity of labelled data in medical imaging by learning representations from unlabelled images. Contrastive and non-contrastive SSL methods learn representations that are similar for pairs of related images.
Aug 26, 2024
Suppose we wish to develop a machine learning model for our favourite medical application, e.g. for detecting a rare disease, or suggesting individualized treatment such as for decreasing cholestorol levels. To do so, we need training data.
Jun 24, 2024
Image-guided surgeries are complex, highly inaccessible environments for researchers hoping to improve interventional healthcare. However, simulations offer readily available, easily controlled environments for replicating these surgeries in a scalable fashion. By enhancing learning- and physics-based simulations, we unlock new possibilities for embodied systems such as automated algorithms and immersive virtual reality (VR) experiences.
Jun 3, 2024
This talk will focus on machine learning applications that have improved our understanding of pulmonary hypertension. About Samer: Dr Samer Alabed is a Clinical Lecturer in Radiology (Assistant Professor) at the University of Sheffield.
May 13, 2024
Die online adaptive Strahlentherapie (ART) ist eine aktuelle Weiterentwicklung der Strahlentherapie zur schnellen Adaptation des Bestrahlungsplans an die Anatomie des Tages, festgestellt mittels CBCT oder MRI auf dem Behandlungstisch zu Beginn einer Fraktion.
May 6, 2024