Computational Anatomy Gateway
Bibliographic record
Abstract
Computational Anatomy (CA) is a discipline focused on the quantitative analysis of the variability in biological shape. The Large Deformation Diffeomorphic Metric Mapping (LDDMM) is the key algorithm which assigns computable descriptors of anatomical shapes and a metric distance between shapes. This is achieved by describing populations of anatomical shapes as a group of diffeomorphic transformations applied to a template, and using a metric on the space of diffeomorphisms. LDDMM is being used extensively in the neuroimaging (www.mristudio.org) and cardiovascular imaging (www.cvrgrid.org) communities. There are two major components involved in shape analysis using this paradigm. First is the estimation of the template, and second is calculating the diffeomorphisms mapping the template to each subject in the population. Template estimation is a computationally expensive problem, which involves an iterative process, where each iteration calculates one diffeomorphism for each target. These can be calculated in parallel and independently of each other, and XSEDE is providing the resources, in particular those provided by the cluster Stampede, that make these computations for large populations possible. Mappings from the estimated template to each subject can also be run in parallel. In addition, the use of NVIDIA Tesla GPUs available on Stampede present the possibility of speeding up certain convolution-like calculations which lend themselves well to the General Purpose GPU computation model. We are also exploring the use of the available Xeon Phi Co-processors to increase the efficiency of our codes. This will have a huge impact on both the neuroimaging and cardiac imaging communities as we bring these shape analysis tools online for use by these communities through our webservice (www.mricloud.org), with the XSEDE Computational Anatomy Gateway providing the resources to handle the computational demands for large populations.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.292 | 0.240 |
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".