MelanoDet: Multimodal Imaging System for Screening and Analysis of Cutaneous Melanoma
Bibliographic record
Abstract
Cutaneous melanoma is one of the most aggressive skin cancers, and early detection is critical for effective treatment. Although traditional imaging techniques such as dermoscopy improve diagnostic accuracy, many cases remain undetected. Recent advancements in non-invasive imaging, including multispectral imaging and infrared thermal imaging, offer new opportunities for early diagnosis. In this study, we introduce MelanoDet, a multimodal imaging system integrating three cameras corresponding to different spectra: visible, near-infrared, and long-wavelength infrared. To ensure accurate lesion inspection, an analytical method based on the optical cameras parameters estimated the cameras’ common field of view, to capture the same region of interest. Since the acquired images differ in resolution, angle, and orientation, a second refined registration step has been applied. A standardized image acquisition protocol was established in collaboration with an experienced team of dermatologists from Elias Emergency University Hospital, Bucharest. The MelanoDet system, along with its protocol and image processing techniques, was validated on 25 cases of suspicious nevi, demonstrating its potential for improving melanoma screening.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".