Deep Kernel Learning based Gaussian Processes for Bayesian Image Regression Analysis
Bibliographic record
Abstract
In neuroimaging applications, different types of regression models have been widely adopted to study the complex associations between images and clinical variables, including scalar-on-image regression, image-on-scalar regression, and image-on-image regression. There are many challenging problems in model interpretations, statistical inferences and predictions in those type of models. To address those issues, we propose a general Bayesian modeling framework for the image regression problems by integrating deep neural networks (DNN) and Gaussian processes (GP) with kernel learning. The proposed framework consists of two levels of hierarchy. At level 1, we assume images as realizations of different GPs and project them on lower dimensional Euclidean spaces using a kernel expansion approach. We adopt a novel DNN based approach to covariance kernel learning of the GPs which provides efficient and accurate image projections. At level 2, we specify the associations between the projected images and other predictors using Bayesian DNNs. We develop efficient variational inference algorithms for posterior computation. We compare the performance of the proposed method with the state-of-the-art methods via extensive numerical experiments on synthetic images from the benchmark datasets as well as analysis of the fMRI data in the large-scale imaging studies.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".