Texture and Structure Incorporated ScatterNet Hybrid Deep Learning\n Network (TS-SHDL) For Brain Matter Segmentation
Bibliographic record
Abstract
Automation of brain matter segmentation from MR images is a challenging task\ndue to the irregular boundaries between the grey and white matter regions. In\naddition, the presence of intensity inhomogeneity in the MR images further\ncomplicates the problem. In this paper, we propose a texture and vesselness\nincorporated version of the ScatterNet Hybrid Deep Learning Network (TS-SHDL)\nthat extracts hierarchical invariant mid-level features, used by fisher vector\nencoding and a conditional random field (CRF) to perform the desired\nsegmentation. The performance of the proposed network is evaluated by extensive\nexperimentation and comparison with the state-of-the-art methods on several 2D\nMRI scans taken from the synthetic McGill Brain Web as well as on the MRBrainS\ndataset of real 3D MRI scans. The advantages of the TS-SHDL network over\nsupervised deep learning networks is also presented in addition to its superior\nperformance over the state-of-the-art.\n
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".