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..
Fully Convolutional Network for Consistent Voxel-Wise Correspondence
Authors: Yungeng Zhang, Yuru Pei, Yuke Guo, Gengyu Ma, Tianmin Xu, Hongbin Zha12935-12942
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on both synthetic and clinically captured volumetric cone-beam CT (CBCT) images show that the proposed framework is effective and competitive against state-of-the-art deformable registration techniques. |
| Researcher Affiliation | Collaboration | 1Key Laboratory of Machine Perception (MOE), Department of Machine Intelligence, Peking University, Beijing, China 2 Luoyang Institute of Science and Technology, Luoyang, China 3 u Sens Inc., San Jose, USA 4 School of Stomatology, Peking University, Beijing, China |
| Pseudocode | No | The paper describes the proposed method in text and diagrams, but does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing open-source code or a link to a code repository for their method. |
| Open Datasets | No | The paper states: 'The training dataset consists of 400 clinically captured CBCT images from orthodontic patients...' and 'we generate a toy dataset with the ground-truth DVFs using synthetic data...'. However, no specific link, DOI, or formal citation to a publicly available version of these datasets is provided. |
| Dataset Splits | No | The paper states, 'The training dataset consists of 400 clinically captured CBCT images...' and 'For testing, we collect a toy dataset with 20 synthetic images and a real dataset with 20 clinically captured images.' It does not explicitly define a separate validation dataset split. |
| Hardware Specification | Yes | The framework is implemented using the open-source Py Torch implementation of convolutional neural networks on an NVIDIA GTX TITAN X GPU. |
| Software Dependencies | No | The paper mentions 'Py Torch implementation' but does not specify a version number for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | We train the network using the ADAM optimizer with a learning rate of 1e-4 and momentums of 0.5 and 0.999. The mini-batch contains three volumes. |