Datasets of "Influence of contrast and texture based image modifications on the performance and attention shift of U-Net models for brain tissue segmentation" Part 9 of 14
Bibliographic record
Abstract
This dataset is part of the work https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full. This is the ninth part of 14 parts of the full dataset (9/14). It contains 3 sets of simulated T1 weighted brain volumes in 3 simulated scanning sequences of spin-echo. The parameters of simulated scanning sequences are respectively repetition time (TR) = 600ms, 700ms, 800ms, and echo time (TE) = 15ms. Under each simulated scanning sequence, there are 500 brain volumes. The segmentation labels for each tissue are contained in the first part which you may find at https://zenodo.org/record/7294916 The simulation process of this dataset involves two processes. The first is to simulate one brain under different simulated scanning sequences. For this, we use BrainWeb https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request. In the custom setting, we use spin-echo and apply image artifact the same as the default setting of this page. The second process is to transform each simulated brain from BrainWeb to different anatomical shapes. We use Human Connectome Project (HCP) 1200 subject data https://www.humanconnectome.org/study/hcp-young-adult and randomly select 500 brains as anatomical references. Other details of this dataset can be found at https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full where the details of the data construction are discussed. All parts of the whole dataset can be found at: Part 1: https://zenodo.org/record/7294916 Part 2: https://zenodo.org/record/7389550 Part 3: https://zenodo.org/record/7390382 Part 4: https://zenodo.org/record/7390741 Part 5: https://zenodo.org/record/7391205 Part 6: https://zenodo.org/record/7393060 Part 7: https://zenodo.org/record/7393174 Part 8: https://zenodo.org/record/7393347 Part 9: https://zenodo.org/record/7394250 Part 10: https://zenodo.org/record/7394667 Part 11: https://zenodo.org/record/7394939 Part 12: https://zenodo.org/record/7395031 Part 13: https://zenodo.org/record/7395620 Part 14: https://zenodo.org/record/7395622
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 0.019 |
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".