Robust automated preclinical fMRI preprocessing via a multi-stage dilated convolutional Swin Transformer affine registration
Bibliographic record
Abstract
Introduction: Accurate preprocessing of functional magnetic resonance imaging (fMRI) data is crucial for effective analysis in preclinical studies. Key steps such as denoising, skull-stripping, and affine registration are essential to align fMRI data with a standard atlas. However, challenges such as low resolution, variations in brain geometry, and limited dataset sizes often hinder the performance of traditional and deep learning-based methods. Methods: To address these challenges, we propose a preclinical fMRI preprocessing pipeline that integrates advanced deep learning modules, with a particular focus on a newly developed Swin Transformer-based affine registration method. The pipeline incorporates our previously established modules for 3D Generative Adversarial Network (GAN)-based denoising and Transformer-based skull stripping, followed by the proposed Multi-stage Dilated Convolutional Swin Transformer (MsDCSwinT) for affine registration. This new registration method captures both local and global spatial misalignments, ensuring accurate alignment with a standard atlas even in challenging preclinical datasets. Results: We validate the pipeline across multiple preclinical fMRI studies and demonstrate that our affine registration module achieves higher average Dice similarity coefficients compared to state-of-the-art methods. Discussion: By leveraging GANs and Transformers, our pipeline offers a robust, accurate, and fully automated solution for preclinical fMRI.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".