GPD-Nodule: A Lightweight Lung Nodule Detection and Segmentation Framework on Computed Tomography Images Using Uniform Superpixel Generation
Bibliographic record
Abstract
Lung nodule detection is key in early diagnosis of lung cancer. Expert radiologists dedicate a significant amount of time and effort to detecting such nodules manually by going through computed tomography scan images slice by slice. This endeavor results in the slow processing of radiological images and possible misdiagnosis due to nodules being tiny by nature. In this paper, we introduce a two-step automatic computer-aided nodule detection method that encompasses a novel uniform superpixel generation algorithm, namely, equivalent patchwise iterative agglomerative clustering. This superpixel generation algorithm can generate the same number of superpixels for every image making it suitable for training neural networks. This method is then coupled with a novel variant of graph neural networks, namely, the curtailed residual nested superpixel propagation network, and an unsupervised region proposal method, namely, pixel nesting region proposal mechanism to detect nodules with high accuracy. The results show an accelerated training process compared to state-of-the-art convolutional neural networks and good generalization capability. Furthermore, the proposed method displays a significant reduction in trainable parameters while achieving high performance in the detection and segmentation of nodules on the Lung Image Database Consortium and Image Database Resource Initiative dataset.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".