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Zsófia Jólesz28/05/2026, 14:00Lecture
The use of hadrons - such as protons, helium, and carbon ions—in radiotherapy requires highly precise Relative Stopping Power (RSP) maps of patient anatomy to minimize range uncertainties. Using the aforementioned hadrons for imaging before the treatment offers higher reconstruction quality and dosimetric advantage in comparison to conventional X-ray CT for this purpose. However, executing the...
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Bence Dudás (Eötvös Loránd University)28/05/2026, 14:20
FLORA: Flow-based Latent-informed Optimization for 3D proton-CT Reconstruction with Spatial Attention. A deep learning framework for conditioned image reconstruction developed for Proton Computed Tomography (pCT). The pipeline utilizes a Varriational Autoencoder-GAN approach to be able to learn biologically correct 3D CT reconstruction, while the latent Flow-matching enables us to condition in...
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Regina Nora Fiam (Eötvös Loránd University)28/05/2026, 14:40
Learning counterfactual representations for cellular perturbations is a fundamental challenge in representation learning, significantly hindered by the fundamentally unpaired nature of interventional data. Current state-of-the-art generative approaches (e.g., GEARS) circumvent this by relying heavily on domain-specific heuristics, such as masking the input space to a subset of highly variable...
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Péter Hunyadi (Pázmány Péter University, Faculty of Information Technology and Bionics)28/05/2026, 15:00Lecture
In most types of cancer, immunosuppression limits an effective anti-cancer immune response. Leukocyte immunoglobulin-like receptor B4 (LILRB4) is an immune checkpoint inhibitor molecule that plays a role in various signaling processes contributing to tumor immune evasion. The aim of our research is to investigate this receptor and its family in colorectal cancer, with a particular focus on the...
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