0) Click on 'Edit' at the top of the page
1) Find your name on the table
2) On the column 'Presentation' next to your name click '+' (it's on the header with the editing tools) → 'Files and Images'→ 'Upload Files'
3) Select your presentation (pptx file) following the naming convention 'Name_FirstName.pptx'
4) After uploading, click on the uploaded file and select 'Show as link'
5) Click 'Save' at the bottom right of the page
| Topic | No | Title | Tutor | Student | Presentation | Background Materials |
|---|---|---|---|---|---|---|
| Architecture Design | 1 | Improving Graph Neural Networks with Simple Architecture Design | Franz Rieger | Berfin Elif Erdoğan | ||
| Road Graphs and Graph-Tensor Encodings | 2 | Sat2Graph: Road Graph Extraction through Graph-Tensor Encoding | Johannes Paetzold | Marcos Balle Sanchez | ||
| Brain Mapping | 3 | 2D histology meets 3D topology: Cytoarchitectonic brain mapping with Graph Neural Networks | Alina Dima | Merve Elif Demirtas | ||
| Registration | 4 | GraphRegNet: Deep Graph Regularisation Networks on Sparse Keypoints for Dense Registration of 3D Lung CTs | Mohammad Farid Azampour | Ha Young Kim | ||
| Registration | 5 | IGCN: Image-to-graph Convolutional Network for 2D/3D Deformable Registration | Mohammad Farid Azampour | Ata Jadid Ahari | ||
| Scene Graphs | 6 | Spatial-Temporal Transformer for Dynamic Scene Graph Generation | Ege Özsoy | Caghan Koksal | ||
| Scene Graphs | 7 | Target Adaptive Context Aggregation for Video Scene Graph Generation | Ege Özsoy | Haichuan Li | ||
| Surgical Scene Graphs | 8 | Global-Reasoned Multi-Task Learning Model for Surgical Scene Understanding | Felix Holm | Yuhan Li | ||
| Surgical Scene Graphs | 9 | Learning and Reasoning with the Graph Structure Representation in Robotic Surgery | Felix Holm | Chengzhi Shen | ||
| Scene Graph Generation | 10 | RelTR: Relation Transformer for Scene Graph Generation | Matthias Keicher | Priyank Upadhya | ||
| Medical Report Generation, Few-shot Learning | 11 | MetaDT: Meta Decision Tree for Interpretable Few-Shot Learning | Matthias Keicher | Chaehyeon Sim | ||
| Anomaly detection, chest x-ray application | 12 | Chest Radiograph Disentanglement for COVID-19 Outcome Prediction | Kamilia Mullakaeva | Diyorbek Rustamov | ||
| GNN Architecture | 13 | Neural Trees for Learning on Graphs | Evin Pınar Örnek | Niklas Vest | ||
| Self-supervision | 14 | Context Matters: Graph-based Self-supervised Representation Learning for Medical Images | Mahsa Ghorbani | Xinyue Zhang | ||
| Anomaly detection using GCN | 15 | Airway Anomaly Detection by Graph Neural Network | Kamilia Mullakaeva | |||
| Explainability of GNNs | 16 | On Explainability of Graph Neural Networks via Subgraph Explorations | Tamara Mueller | Malika Sanhinova | ||
Scene Graphs | 17 | Decoupling Object Detection from Human-Object Interaction Recognition | Tobias Czempiel | Xinyu Chen | ||
Dynamic Scene Graphs | 18 | Exploiting Long-Term Dependencies for Generating Dynamic Scene Graphs | Tobias Czempiel | |||
| Pooling | 19 | Hierarchical Graph Representation Learning with Differentiable Pooling | Anees Kazi | Daniel Stoll | GitHub | |
| Uncertainty | 20 | Edge-variational Graph Convolutional Networks for Uncertainty-aware Disease Prediction | Anees Kazi | Rafael Cabral Muchacho |