Multi-label classification analysis with modified C-Tran on SCIN dataset ,
Bibliographic record
Abstract
Skin conditions affect millions of people globally, with symptoms appearing in different body areas. Technological advancements have brought diverse data types, including situations where an image depicting a skin condition can be assigned multiple labels. The Classification Transformer (C-Tran) method, which utilizes transfer learning and transformers, was developed for multi-label classification. Recently, Google introduced a new dataset called SCIN (Skin Condition Image Network), which aims to provide diverse data on skin conditions. This research aimed to use the C-Tran method for the multi-label classification of skin conditions with the SCIN dataset while incorporating additional metadata inputs to improve the metric results. The results show that the multi-label classification process using metadata is far superior to the model without metadata. For example, In the mAP metric, models that utilized metadata scored 82.37, whereas models without metadata only scored 47.02. Similarly, models with metadata achieved 70.83% in the accuracy metric, while models without metadata achieved only 34.72%. Out of the 10,379 data points available with metadata in the SCIN dataset, only 718 were actually utilized for the classification task. It is thought that the inaccurate prediction outcomes are due to unreliable data, even with a confidence level of 4. In this analysis, two metadata categories stood out the most in terms of different measurements: the body part and symptoms metadata categories from the SCIN dataset. With just the body part and symptoms metadata groups, the mAP results achieved a 74.23%, accuracy at 63.89%, CF1 at 68.79%, and OF1 at 73.13%.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.005 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".