Five papers accepted at MICCAI 2023 workshops

Hannah Eichhorn, Veronika Spieker, Cosmin I. Bercea, Daniel M. Lang, Maxime Di Folco

Sep 14, 2023 1 min read

Five papers have been accepted for publication at workshops associated with the 26th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2023, which will be held from October 8th to 12th 2023 in Vancouver, Canada.

Interested to hear more about our work? Then join us at the following workshops:

Veronika Spieker will be at the DGM4 workshop to talk about Neural Implicit Representations for Abdominal MR Reconstruction on October 8, at 10:25.
Hannah Eichhorn presents her work on physics-aware motion simulation for T2*-weighted MRI at the SASHIMI workshop on October 8, at 14:40. Check out the preprint for more information!
Maxime Di Folco presents at the STACOM workshop on October 12, at 11:15 the work of Josh Stein on “Sparse annotation strategies for segmentation of short axis cardiac MRI” (preprint).
Cosmin Bercea will talk about Bias in Unsupervised Anomaly Detection at the FAIMI workshop on October 12, at 2:50 PDT.
Daniel Lang will talk about Anomaly Detection in Non-Contrast Enhanced Breast MRI at the CaPTion workshop on October 12.

Hannah Eichhorn

Doctoral Researcher

Hannah Eichhorn’s research focuses on deep learning-based reconstruction of multi-parametric brain MRI.

Veronika Spieker

Doctoral Researcher

Veronika Spieker’s interests include AI-based methods for MR reconstrution and motion correction.

Cosmin I. Bercea

Research Scientist

I am a postdoctoral researcher specializing in vision and multimodal learning for medical image analysis, with the current focus on developing vision-language models for generative downstream tasks.

Daniel M. Lang

Research Scientist

My research focuses on the application of deep learning models for problem settings in cancer imaging.

Maxime Di Folco

Research Scientist

My research interest is the study of the cardiac function via machine learning methods, in particular representation learning methods that aim to acquire low dimensional representation of high dimensional data. I have a strong interest in cardiac remodelling (adaptation of the heart to its environment or a disease), notably the study of the deformation and shape aspects.