Deep learning for clinical and pre-clinical research Image credits: NVIDIA;
Center for Translational Cancer Research (TranslaTUM)
Klinikum rechts der Isar, Trogerstr 1, 81675 Munich
(I always respond; if not, your email got stuck in a spam filter)
My research interest is motivated by the tremendous potential impact of making Deep Learning-based computer vision applicable in everyday settings in clinical and pre-clinical research. This entails developing deep convolutional networks for detection, localization, and segmentation tasks in medical images of various modalities. Ensuring applicability in everyday settings typically requires elaborate strategies to deal with limited availability of annotated data, for instance making use of Transfer Learning or Data Augmentation.
I cooperate with Ali Ertürk's Lab at the Institute for Stroke and Dementia Research at Klinikum Großhadern. This work comprises research questions like automatic image segmentation and classification problems for general 3D anatomy, but also specific questions in the fields of stroke, dementia, and cancer.
DeepMACT - Deep learning based Metastasis Analysis in Cleared Tissue
The DeepMACT pipeline is an end-to-end procedure that enables highly automated detection and characterization of tumor micro-metastasis in large volumetric scans of whole cleared mice. This pipeline comprises two core technologies: DISCO tissue clearing and deep learning. For details, please refer to web resources and the article published in Cell (2019)
We are inviting applications for several exciting research opportunities for Master students with our group. Please also feel free to reach out if you are interested in a Bachelor thesis, research project, etc. The projects are intended for students in Informatics, Electrical Engineering, or related fields. Please refer to the project descriptions below for more detailed information.
We expect all students to have:
Prior theoretical knowledge on deep learning
Some practical experience with deep learning (PyTorch or TensorFlow)
Good Python coding skills
Ideally experience in computer vision / medical image analysis
Deep learning-based tumor detection in 3D scans
Deep transfer learning for 3D brain vessel segmentation
Deep learning-based tracing of single neurons and neural pathways
Distributed deep learning for large-scale medical image analysis
Deep learning-based 3D segmentation of prostate lesions
Analysis of CT scans from automotive OEM with deep learning
Deep learning-based multi-organ segmentation for MRI & CT
Deep learning-based multi-organ segmentation for 3D microscopy