Collaborations/student theses
This page contains a partial list of collaborations with companies, organizations and student theses/internships organized by topic.
Medical imaging
1) Deep Learning Segmentation Of Heart Substructures (L. Nürnberg, Maastro Clinic)
2) Volume effect in hand-crafted Radiomics feature (A. Romita, Maastro Clinic)
3) Predicting Tumor Hypoxia Map from FDG-PET/CT Images using GANs (C. Rao, Maastro Clinic)
4) A Comparative Study of Visual Transformers for Thoracic Disease Classification (D. Demirkiran)
5) 3D Unpaired GANs in Solving the Domain Shift Problem Encountered Across Medical Imaging Scenarios (H. Ibrahim, Maastro Clinic)
6) Deep Learning approach on Survival Outcome Prognostication for Oesophageal Cancer (V. Prudente, Maastro Clinic)
7) Automated Diagnostic System of Skin Cancer using Deep Convolutional Neural Networks on Dermoscopic Images (W. Aljbawi)
8) Deep Learning Techniques for Detection and Diagnosis of Brain Metastases using Medical Image Analysis (M. Pliusnova)
9) Vision Transformer for Brain Tumor Classification (E. Simon)
10) Deep learning based auto-segmentation of Head-and-Neck cancer using bi-modal medical image data (C. Rao, Maastro Clinic)
11) Deep Learning for domain adaptation and synthetic imaging of medical images (J. Posch, Maastro Clinic)
12) Automatic segmentation of organs at risk in head and neck region using 3D Unets (S. Datta, Maastro Clinic)
Precision farming
1) Detection And Classification Of Insects Catched By Yellow Sticky Traps (M. Deserno, Augmenta)
2) Real-time optical flow estimation with cost learning (F. Debrauwer, Augmenta)
3) Mask R-CNN for apple segmentation (C. Callum)
4) Improving accuracy and efficiency in plant detection on a novel, benchmarking real-world dataset (L. Ohnemüller)
5) Approximated polynomial complexity Multiple Hypothesis Tracker with rigidity constrained Extended Kalman Filter (M. Mitchell)
6) Deep Learning for Fruits Image Recognition (N. Gheza)
7) Broccoli Head Classification and Segmentation (D. Acquaviva)
8) Soil detection for precision agriculture (F. Debrauwer, Augmenta)
9) Grape detection in real-world conditions (N. Oikonomou, Augmenta)
Computer Vision
1) POG: Proportionality-based Occlusion Grid contextual explanations for semantic segmentation (A. Herbert)
2) Aerial to street view image translation using cascaded conditional GANs (K. Singh)
3) Automatic Emotion Analysis Using A Deep Learning Fusion Model On Audio And Video Data (T. Cuong)
4) Counting People in the Street from Video (E. Korzen)
5) Deep Learning for Robust & Fast end-to-end Tone Mapping (R. Montulet)
6) Image Classification with Convolutional Neural Networks and Cubical Persistence (G. Giouvanis, Faunawatch)
7) Fine Grained Action Recognition of Skateboarding Tricks (F. Calsius)
8) Unsupervised Domain Adaptation for Image Classification (D. Acquaviva)
Satellite imaging
1) A new approach to automatic neural search (NAS) applied to satellite images of farms (A. Roelofs)
2) Possibilities to improve the performance and robustness of U-Nets for image segmentation in satellite images with a focus on attention mechanisms and transfer learning (P. Rinkwitz)
3) Super-resolution and de-noising on XMM-Newton images using convolutional neural networks (S. Sweere, ESA-ESAC European Space Astronomy Center)
4) Comparison and Refinement of Convolutional Neural Network Techniques for Urban Change Detection (C. Tod)
5) Detecting Land Cover Changes in Satellite Images Using a Modified U-Net (E. Rauth)
6) Wetland Classification in Permafrost using Deep Learning for Estimation of GHG (F. Rustemeyer, Stockholm Environment Institute)