Talks and presentations

Taming Detection Transformers for Medical Object Detection

Talk, BVM 2023 - Bildverarbeitung fuer die Medizin, Braunschweig, Germany

Presented our work on DEtection TRansformers (DETR) for medical object detection. More info can be found in our publication. Our paper ranked 3rd for the Best Paper Award.

Accurate Detection of Mediastinal Lesions with nnDetection

Talk, MICCAI 2022 - 25th International Conference on Medical Image Computing & Computer Assisted Intervention, Singapore

Presented our solution (3rd Rank) of the Mediastinal Lesion Detection Detection (2022) challenge. More info can be found in our short description.

nnDetection: A Self-configuring Method for Medical Object Detection (Abstract)

Talk, BVM 2022 - Bildverarbeitung fuer die Medizin, Heidelberg, Germany

Presented our submitted abstract about nnDetection: A Self-configuring Method for Medical Object Detection. Presentation was awarded the Best Presentation Award.

Self-configuring Methods for Deep Learning-based Biomedical Image Analysis

Talk, Analytica 2022, Muenchen, Germany

Presented our work on self-configuring methods for deep learning-based biomedical image segmentation (nnU-Net) and object detection (nnDetection).

What’s Next: Technological Advancement for AI in Radiology On the horizon

Talk, 102. Deutscher Roentgen Kongress, Virtual

Presented selected topics about recent technological advancements in the field of artificial intelligence in radiology. The following areas were presented:

• Transformers: Closing the Gap between Natural Language Processing (NLP) and Computer Vision (CV) in Medical Image Computing
• Semi-Supervised Learning: How to learn from unlabelled Data?
• Federated Learning: Big Data without Data Transfer
• Generalisation to new data sets via self-configuring methods

Advanced Deep Learning Tutorial: Medical Object Detection

Talk, BVM 2021 - Bildverarbeitung fuer die Medizin, Virtual due to COVID (supposed to be presented in Regensburg, Germany)

The remarkable rise of deep learning has led to an overwhelming amount of new papers coming up by the week. This tutorial intends to filter out the research most relevant for the medical image computing (MIC) community and present it in a structured and understandable form. It will cover recent developments related to common tasks in the community (e.g. segmentation, detection), but will also discuss methods that are currently gaining traction. Basic knowledge of neural networks and deep learning is recommended.

Retina U-Net for aneurysm detection in MR images

Talk, MICCAI 2020 - 23rd International Conference on Medical Image Computing & Computer Assisted Intervention, Virtual due to COVID (supposed to be presented in Lima, Peru)

Presented our winning solution (Task 1: Detection Task) of the Aneurysm Detection And segMentation Challenge (2020) challenge. Our solution used a Retina U-Net architecture with a Feature Pyramid Network to detection aneurysms in the provided MR images. More info can be found in our short description.