Binomial classification based on DLENE features in sparse representation: Application in kidney detection in 3D ultrasound
Bibliographic record
Abstract
Sparse representation-based classification (SRC) has been recently attracted a great interest among the signal processing society. SRC applies a discriminative representation using training samples to separate signals into their classes. In existing SRC methods, the dictionary size, which highly affects the performance, is manually set. Moreover, they are linear classifiers, and thus, they are not suitable for classifying nonlinear problems. In this paper, we propose a new classification method by cascading a dictionary learning and the neural network to take the advantages of both methods. We use dictionary learning with efficient number of elements (DLENE) to extract discriminative features. We also use the proposed binomial classifier to detect kidneys in 3D ultrasound images. A set of Caltech-101 images are used to compare the proposed method with the state-of-the-art. The proposed kidney detection is evaluated by a set of ultrasound volumes. The results confirm the superiority of our proposed method.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".