Self-CephaloNet: a two-stage novel framework using operational neural network for cephalometric analysis
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Cephalometric analysis is essential for the diagnosis and treatment planning of orthodontics. In lateral cephalograms, however, the manual detection of anatomical landmarks is a time-consuming procedure. Deep learning solutions hold the potential to address the time constraints associated with certain tasks; however, concerns regarding their performances have been observed. To address this critical issue, we propose an end-to-end cascaded deep learning framework (Self-CephaloNet) for the task, which demonstrates benchmark performance over the ISBI 2015 dataset in predicting 19 cephalometric landmarks. Due to their adaptive nodal capabilities, Self-ONN (self-operational neural networks) demonstrates superior learning performance for complex feature spaces over conventional convolutional neural networks. To leverage this attribute, we introduce a novel self-bottleneck in the HRNetV2 (high-resolution network) backbone, which has exhibited benchmark performance on our landmark detection task. Our first-stage result surpasses previous studies, showcasing the efficacy of our singular end-to-end deep learning model, which achieves a remarkable 70.95% success rate in detecting cephalometric landmarks within a 2-mm range for the Test1 and Test2 datasets which are part of ISBI 2015 dataset. Moreover, the second stage significantly improves overall performance, yielding an impressive 82.25% average success rate for the datasets above within the same 2-mm distance. Furthermore, external validation has been conducted using the PKU cephalogram dataset. Our model demonstrates a commendable success rate of 75.95% within the 2-mm range.
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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 | 0.004 |
| Science and technology studies | 0.001 | 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 it