Skin Cancer Detection using Ensemble Learning and Grouping of Deep Models
Bibliographic record
Abstract
Melanoma remains the most dangerous form of skin cancer which has a high mortality rate. When detect early, melanoma can be easily cured and millions of lives might be saved. The use of automatic detection models in clinical decision support can increase the ability to address this issue and improve survival rates. In this work, we proposed an automated pipeline for melanoma detection, which combines the predictions of deep convolutional neural network models through ensemble learning techniques. Furthermore, our automated pipeline includes various strategies such as image augmentation, upsampling, image cropping, digital hair removal and class weighting. Our pipeline was trained and tested using the image data acquired from the Society for Imaging Informatics in Medicine and the International Skin Imaging Collaboration SIIM-ISIC 2020. Our proposed pipeline has demonstrated a high performance compared to the other state-of-the-art pipelines for melanoma disease prediction with an accuracy of 97.77% and an AUC of 98.47%.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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 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".