Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Deep Learning in Medical Image Registration: Magic or Mirage?
Authors: Rohit Jena, Deeksha Sethi, Pratik Chaudhari, James Gee
NeurIPS 2024 | Venue PDF | 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 EMAIL, EMAIL |
| 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. |