Spot the Difference: Detection of Topological Changes via Geometric Alignment
Authors: Per Steffen Czolbe, Aasa Feragen, Oswin Krause
NeurIPS 2021 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our approach is validated on two tasks and datasets: detection of topological changes in microscopy images of cells, and unsupervised anomaly detection brain imaging. |
| Researcher Affiliation | Academia | Steffen Czolbe Department of Computer Science University of Copenhagen EMAIL Aasa Feragen DTU Compute Technical University of Denmark EMAIL Oswin Krause Department of Computer Science University of Copenhagen EMAIL |
| Pseudocode | No | The paper describes algorithms but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The implementation is available at github.com/Steffen Czolbe/Topological Change Detection. |
| Open Datasets | Yes | For the control set, we combine T1 weighted MRI scans of the healthy subjects from the ABIDE I [11]1, ABIDE II [12] and OASIS3 [27] studies. For the tumor set we use MRI scans from the Bra TS2020 brain tumor segmentation challenge [3, 4, 34] |
| Dataset Splits | Yes | the remaining images of the control dataset are split 2381/149/162 for train/validation/test. |
| Hardware Specification | Yes | We train each model on a single Titan RTX GPU |
| Software Dependencies | No | The paper mentions 'ADAM [25]' (optimizer) and 'U-Net [44]' (architecture) but does not provide specific version numbers for software libraries or dependencies (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | For training, the optimization algorithm is ADAM [25] with a learning rate of 10 4. Regularization of all models is performed by applying an L2-penalty to the weights with a factor of 0.01 for the cell dataset and 0.0005 for the brains. ... averaged over the mini-batch of 32 image pairs. |