Deep Learning in Medical Image Registration: Magic or Mirage?
Authors: Rohit Jena, Deeksha Sethi, Pratik Chaudhari, James Gee
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | This hypothesis is thoroughly validated with state-of-the-art classical and learning-based methods. |
| Researcher Affiliation | Academia | Rohit Jena1,4 Deeksha Sethi1 Pratik Chaudhari1,2, James C. Gee1,3,4, 1Computer and Information Science 2Electrical and Systems Engineering 3Radiology 4 Penn Image Computing and Science Laboratory {rjena, deesethi, pratikac}@seas.upenn.edu, gee@upenn.edu |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | We make all evaluation scripts and trained models public2 to encourage fairness and transparency in evaluations. 2https://github.com/rohitrango/Magic-or-Mirage/ |
| Open Datasets | Yes | We consider four brain datasets OASIS, LPBA40, MGH10, and IBSR18 |
| Dataset Splits | Yes | We split the OASIS dataset into a training set of 364 images and a validation set of 50 images. |
| Hardware Specification | Yes | All experiments are run on a cluster with 2 AMD EPYC 7713 CPUs and 8 NVIDIA A6000 GPUs. |
| Software Dependencies | No | The paper lists various state-of-the-art classical and DLIR baselines (e.g., ANTs, Nifty Reg, Synth Morph, Trans Morph) but does not specify software versions for any of them or for any other key dependencies. |
| Experiment Setup | No | For all DLIR methods, we use pretrained models if they are trained with Eq. (1), or train them with the architecture and hyperparameters provided in their original source code. [...] For all classical methods, we follow their recommended hyperparameters and run till convergence. |