Multi-label classification analysis with modified C-Tran on SCIN dataset ,
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.005 | 0.001 |
| 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 it