Benchmarking the Robustness of Deep Learning Segmentation Models to Out-of-distribution and Corrupted Data in MR Neuroimaging
Bibliographic record
Abstract
A principal challenge in the large-scale deployment of deep neural networks (DNNs) in neuroimage analysis is the potential for shifts in signal-to-noise ratio, contrast, resolution, and presence of artifacts (i.e., distribution shifts) from site to site due to variances in scanners and acquisition protocols. Currently, there are no benchmarking frameworks to assess the robustness of DNN-based models to distribution shifts in MRI, and accessible multi-site benchmarking datasets are still scarce or task-specific. To address these limitations, we develop a platform providing modules for generating benchmarking datasets using transforms that model distribution shifts in MRI and newly derived benchmarking metrics. We apply our methodology to hippocampus, ventricle, and white matter hyperintensity segmentation in several large studies, demonstrating that modern DNNs are highly susceptible to distribution shifts in MRI and that data augmentation strategies and architectural design considerations (e.g., U-Nets vs. transformer-based models) have significant implications for robustness to particular transforms.
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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 | 0.012 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".