CoMIR: Contrastive Multimodal Image Representation for Registration
Authors: Nicolas Pielawski, Elisabeth Wetzer, Johan Öfverstedt, Jiahao Lu, Carolina Wählby, Joakim Lindblad, Natasa Sladoje
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We assess the extent of achieved rotational equivariance and the stability of the representations with respect to weight initialization, training set, and hyperparameter settings, on a remote sensing dataset of RGB and near-infrared images. We evaluate the learnt representations through registration of a biomedical dataset of bright-field and second-harmonic generation microscopy images; two modalities with very little apparent correlation. |
| Researcher Affiliation | Academia | Nicolas Pielawski , Elisabeth Wetzer*, Johan Öfverstedt, Jiahao Lu, Carolina Wählby, Joakim Lindblad, Nataša Sladoje Dept. of Information Technology, Uppsala University, Sweden |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at: https://github.com/MIDA-group/Co MIR. |
| Open Datasets | Yes | Zurich Dataset. The open Zurich dataset [57] consists of 20 aerial images of the city of Zurich of about 930 940px. |
| Dataset Splits | Yes | The training set consists of 40 image pairs of size 834 834px which are center-cropped from the corresponding original images. The validation set for the CNN training consists of another 25 such pairs, a tuning set for registration parameters of additional 7 pairs, and the test set for evaluation of another 134 pairs. |
| Hardware Specification | No | The paper mentions 'training (GPU, 1345s)' and 'inference (CPU, 5s/image)' but does not provide specific hardware details such as GPU or CPU models. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers, such as programming language versions or library versions (e.g., Python 3.8, PyTorch 1.9). |
| Experiment Setup | Yes | We set the temperature to τ = 0.5. We use 46 negative samples. ... For both datasets patches of the size 128 128px were chosen. ... The SHG images were preprocessed by applying a log-transform log(1 + x) with x [0, 1]. |