Discovery Radiomics via Deep Multi-Column Radiomic Sequencers for Skin Cancer Detection
Bibliographic record
Abstract
While skin cancer is the most diagnosed form of cancer in menand women, with more cases diagnosed each year than all othercancers combined, sufficiently early diagnosis results in very goodprognosis and as such makes early detection crucial. While radiomicshave shown considerable promise as a powerful diagnostictool for significantly improving oncological diagnostic accuracy andefficiency, current radiomics-driven methods have largely rely onpre-defined, hand-crafted quantitative features, which can greatlylimit the ability to fully characterize unique cancer phenotype thatdistinguish it from healthy tissue. Recently, the notion of discoveryradiomics was introduced, where a large amount of custom, quantitativeradiomic features are directly discovered from the wealth ofreadily available medical imaging data. In this study, we presenta novel discovery radiomics framework for skin cancer detection,where we leverage novel deep multi-column radiomic sequencersfor high-throughput discovery and extraction of a large amount ofcustom radiomic features tailored for characterizing unique skincancer tissue phenotype. The discovered radiomic sequencer wastested against 9,152 biopsy-proven clinical images comprising ofdifferent skin cancers such as melanoma and basal cell carcinoma,and demonstrated sensitivity and specificity of 91% and 75%, respectively,thus achieving dermatologist-level performance andhence can be a powerful tool for assisting general practitionersand dermatologists alike in improving the efficiency, consistency,and accuracy of skin cancer diagnosis.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".