Adaptive Fuzzy Evidential Reasoning for Automated Brain Tissue Segmentation
Bibliographic record
Abstract
This paper presents an adaptive fuzzy evidential reasoning approach, for segmenting multi-modality MR brain images. A novel fuzzy evidence structure model is proposed under the assumption that each information source provides two types of evidence: probabilistic evidence and fuzzy evidence. A new information measure, called hybrid entropy, is employed for evaluating the overall uncertainty contained in a fuzzy evidence structure. For adaptive reasoning, two discounting strategies are included. To handle conflict between the probabilistic evidence and the fuzzy evidence, local discounting takes into account Kullback-Leibler distance between the two types of evidence. Global discounting takes into account source quality, in terms of Shannon entropy and hybrid entropy, for dealing with conflict of sources. To demonstrate its effectiveness, the approach is applied to segmenting multi-modality MR brain images. It is concluded that the proposed approach performs better than Kmean clustering, majority voting, fuzzy set operators, and Bayesian approach.
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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 | 0.003 | 0.008 |
| 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) | 0.001 | 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".