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..
Bidirectional Recurrence for Cardiac Motion Tracking with Gaussian Process Latent Coding
Authors: Jiewen Yang, Yiqun Lin, Bin Pu, Xiaomeng Li
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To evaluate the performance of GPTrack in cardiac motion tracking, we conduct experiments based on 3D Echocardiogram videos [17, 18] and 4D temporal MRI image [19]. Results in Tables 1, 2 and 3, show the GPTrack enhance the accuracy of motion tracking performance in a clear margin, without substantially increasing the computational cost in comparison to other state-of-the-art methods. |
| Researcher Affiliation | Academia | Jiewen Yang Yiqun Lin Bin Pu Xiaomeng Li B The Hong Kong University of Science and Technology EMAIL EMAIL |
| Pseudocode | No | The paper describes its methodology using mathematical formulations and architectural diagrams (Figures 2 and 3), but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code is available at: https://github.com/xmed-lab/GPTrack. |
| Open Datasets | Yes | Cardiac UDA [17]. The Cardiac UDA dataset collected from two medical centers consists of 314 echocardiogram videos from patients. CAMUS [18]. The CAMUS dataset provides pixel-level annotations for the left ventricle, myocardium, and left atrium in the Apical two-chamber view... ACDC [19]. The ACDC dataset consists of 100 4D temporal cardiac MRI cases. |
| Dataset Splits | Yes | In Cardiac UDA, we split the dataset into 8 : 2 for training and validation. During testing, we reported results in 10 fully annotated videos. In the CAMUS [18] dataset, videos without annotation are used for only training, while we randomly split the remaining 450 annotated videos into 300/50/100 for training, validation and testing. |
| Hardware Specification | Yes | For all experiments, We use Intel(R) Xeon(R) Platinum 8375C with 1 RTX3090 for both training and inference. |
| Software Dependencies | No | We trained the model using the Adam optimizer with betas equal to 0.9 and 0.99. |
| Experiment Setup | Yes | We trained the model using the Adam optimizer with betas equal to 0.9 and 0.99. The training batch size of the model was set to 1. We trained for a total of 1000 epochs with an initial learning rate of 5e 4 and decay by a factor of 0.5 in every 50 epochs. During training, for Cardiac UDA [17] and CAMUS [18], we resized each frame to 384 384 and then randomly cropped them to 256 256. All frames were normalized to [0,1] during training. In temporal augmentation of datasets [17, 18], we randomly selected 32 frames from an echocardiogram video with a sampling ratio of either 1 or 2. For ACDC [19], we resampled all scans with a voxel spacing of 1.5 1.5 3.15mm and cropped them to 128 128 32, normalized the intensity of all images to [-1, 1]. For spatial data augmentation of all datasets, we randomly applied flipping, rotation and Gaussian blurring. |