Towards comparable MRI: The role of open-source software and community standards
Bibliographic record
Abstract
This talk was presented at the Quantitative MRI for In Vivo Histology From Six Perspectives session during OHBM 2022, Glasgow, UK. Details about the session are available here. To introduce qMRI, I used jellybeans, the beanboozled challenge and some interesting food engineering studies! Looks like qMRI does a good job for soft confections, but when it comes to soft tissue, we found ourselves in a maze of variability. If qMRI is a collection of amazing methods trapped in a maze of variability, how do we find our way out? I argue that open-source software, community data standards, vendor-neutral pulse sequences and reproducible workflows can show us the way forward.
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 | 0.222 | 0.364 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.026 | 0.052 |
| Open science | 0.016 | 0.038 |
| Research integrity | 0.011 | 0.022 |
| Insufficient payload (model declined to judge) | 0.018 | 0.020 |
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".