Fusing Superpixel Graph Propagation and Positional Convolutions for Small Object Detection in Computed Tomography Scan
Bibliographic record
Abstract
Small object detection in radiological images has been a key challenge in the field of medical diagnosis for the last decade. Radiological modalities such as computed tomography scan imaging are often used to evaluate the condition of a patient. These modalities can capture anomalies from various parts of the body which are then analyzed by radiologists to identify tumors, stones, nodules, etc. However, it becomes a strenuous task for any radiologist to accurately identify tiny abnormalities contained within the radiological images. In this paper, a two-step lightweight computer-aided anomaly detection method is proposed that is suitable for small object detection in computed tomography scan images. The proposed method leverages the power of uniform superpixel generation, graph propagation, and positional convolutions to detect anomalies in computed tomography scan images with great accuracy. Furthermore, experiments conducted on imaging datasets from two distinct organs of the body that is the lungs and the kidneys, show the effectiveness of the proposed method in terms of small object detection in medical imaging.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 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".