Bibliographic record
Abstract
Medical imaging is one of the most common areas of computer vision research and algorithm development. FLAIR-MRI is particularly useful in highlighting damaged and necrotic tissue in brain images due to high contrast and resolution. Image registration is a method of warping images to the same geometric space to quantify tissue changes with accuracy. With advances in deep-learning via convolutional neural networks, complex problems can now move closer to some semblance of a solution with purpose-built and domain specific models. To overcome the non-learnable nature of current registration algorithms, ideas are adapted from video processing solutions of calculating optical flow between temporally spaced frames using unsupervised CNN-based methods to warp moving medical images to a fixed image space. The proposed total network loss combines pixelwise photometric differences, flow smoothness, and intensity correlation. Registration accuracy of the proposed and four other registration algorithms is measured by examining tissue integrity, pixelwise alignment, orientation, and global intensity similarity. The results, tested on two large FLAIRMRI datasets consisting of 700 and 4000 brain volumes, show that the optical-flow registration technique is able to obtain maximal alignment while maintaining structural tissue integrity.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".