Multi-Dataset Training for Skin Lesion Classification on Multimodal and Multitask Deep Learning
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
According to the World Health Organization, skin cancer represents approximately one third of every diagnosed cancer, reaching over 3 million cases over the world, annually. Similar to other types of cancer, though, early diagnosis is key for a good outcome, and computer-aided diagnosis has shown great promise in such task. In this paper we improve the results of previous work on skin lesion diagnosis by using a deep convolutional neural network trained on multimodal data, namely macroscopic and dermoscopic image and metadata. For a deep learning approach is important to have a large number of samples, which EDRA dataset does not present. We have improved the results of previous work in the field of multimodal and multitasking for skin lesion classification by performing transfer learning using similar datasets, which are predicting different skin conditions. By pre-training on datasets which belong to a similar domain, the network learns useful features which enhances the performances of the network.
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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 | 0.000 | 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 it