Th: Master thesis, RP: Research Project (guided research, interdisciplinary research)
Research Area: [Reliable AI], [Spatio-temporal learning], [Data-efficient learning], [Application]
[Th15] DWI data quality enhancement for automated probabilistic tractography (Master thesis at TU Munich) [Maria] [Application]
[Th14] Selective Predictor towards Human-like Deep Neural Networks (Master thesis at TU Munich) [Mirac Ayberk Sanisoglu] [Reliable AI]
[Th13] Surgical Workflow Analysis under Limited Annotation (Master thesis at TU Munich) [Fernando Benito Abad; co-supervision with Tobias Czempiel] [Data-efficient learning][Spatio-temporal learning]
[Th12] Interactive Segmentation for Improving Infection Quantification in CT scans (Master thesis at TU Munich) [Michelle Foo] [Reliable AI]
[Th11] EvaluationNet: Human Skill Evaluation with Deep Neural Networks (Master thesis at TU Munich) [Hooriya Anam] [Spatio-temporal learning]
[Th10] Multitask Learning for Longitudinal CT-based Covid Analysis (Master thesis at TU Munich) [Najeeb Khan] [Spatio-temporal learning]
[Th9] Semi-supervised Active Learning (Master thesis at TU Munich) [Felix Buchert] [Data-efficient learning]
[RP6] Quantifying Longitudinal Changes of Pathology from Covid-19 CTs (Research Project at TU Munich) [Leili Goli; co-supervision with Askan Khakzar] [Spatio-temporal learning]
[RP8] Interpreting Covid-19 Prediction Models using Information Bottleneck [MLMI students; Co-supervision with Askan Khakzar] [Reliable AI]
[RP7] Dissection of Covid-19 Prediction Models [MLMI students; co-supervision with Askan Khakzar] [Reliable AI]
[Th8] MR-TRUS Deformable Registration using Deep Generative Models (Master Thesis at TU Munich) [Kristina Mach; Co-supervision with Farid Azampour] [Application]
[Th7] Deep Generative Model for Longitudinal Analysis (Master Thesis at TU Munich) [Umut Küçükaslan] [Spatio-temporal learning]
[Th6] Robust Training of Neural Networks under Noisy Labels (Master Thesis at TU Munich) [Cagri Yildiz, Co-supervision with Dr. Shadi Albarqouni] [Data-efficient learning]
[RP5] Analysis of Adversarial Examples with Feature Attribution Methods (Research Project at TU Munich, 2020S) [Youssef Zidan] [Reliable AI]
[RP4] EfficientNet? with Robust Training [MLMI students] [Data-efficient learning]
[Th5] Disentangled Representation Learning of Medical Brain Images using Flow based Models (Master Thesis at TU Munich, 2020S) [Aadhithya Sankar; Co-supervision with Matthias Keicher; Collaboration with DeepC GmbH] [Reliable AI]
[Th4] Self-supervised Learning for Out-Of-Distribution Detection in Medical Applications (Master Thesis at TU Munich, 2020S) [Abinav Ravi Venkatakrishnan; Collaboration with DeepC GmbH] [Data-efficient learning]
[Th3] Continual Learning with Less Forgetting Strategy for Medical Applications (Master Thesis at TU Munich, 2020S) [Afshar Kakei] [Data-efficient learning]
[Th2] Development of Spatio-temporal Segmentation Model for Tumor Volume Calculation in Micro-CT (Master Thesis at TU Munich, 2020S) [Tetiana Klymenko; co-supervision with Dr. Shadi Albarqouni, Dr. Guillaume Landry] [Spatio-temporal learning]
[RP3] Handling Imbalanced Data Problem in Chest X-ray Multi-label Classification (Guided Research Project at TU Munich, 2019W) [Mirac Ayberk Sanisoglu; co-supervision with Askan Khakzar] [Reliable AI]
[RP2] Understanding Medical Images to Generate Reliable Medical Report (Guided Research Project at TU Munich, 2019W) [Hossain Shaikh Saadi; co-supervision with Dr. Shadi Albarqouni] [Reliable AI]
[Th1] Learning to Learn: Which Data We Have to Annotate First in Medical Applications? (Master Thesis at TU Munich, 2019W) [Farrukh Mushtaq] [Data-efficient learning]
[RP1] Multimodal Longitudinal Multiple Sclerosis Lesion Segmentation (Guided Research Project at TU Munich, 2019W) [Stefan Denner, Moiz Sajid] [Spatio-temporal learning]