AFLN-DGCL: Adaptive Feature Learning Network with Difficulty-Guided Curriculum Learning for skin lesion segmentation
Bibliographic record
Abstract
Background and problems: Automated skin lesion segmentation is a crucial step in the whole computer-aided (CAD) skin disease process. Recently, the fully convolutional network (FCN) has achieved outstanding performance on this task. However, it remains challenging because of three problems: (1) the difficult cases on dermoscopy images, including low contrast lesion, bubble and hair occlusion cases; (2) the overfitting problem of FCN-based methods that is caused by the imbalanced training of difficult samples and easy samples; (3) the over-segmentation problem of FCN-based methods. Method: This work proposes a new skin lesion segmentation framework. Specifically, feature representations from dermoscopy images are learned by the Adaptive Feature Learning Network (AFLN). An ensemble learning method is introduced to build a fusion model, enabling the AFLN model to capture the multi-scale information. We propose a Difficulty-Guided Curriculum Learning (DGCL) with step-wise training strategy to handle the overfitting problem caused by the imbalanced training. Finally, a Selecting-The-Biggest-Connected-Region (STBCR) is proposed to alleviate the over-segmentation problem of the fusion model. Experimental results: The method performance is compared using the same defined metrics (DICE, JAC, and ACC) with other state-of-the-art works on publicly available ISIC 2016, ISIC 2017, and ISIC 2018 databases, and results (0.931, 0.875, and 0.966), (0.881, 0.807, and 0.948), and (0.920, 0.856, and 0.966) illustrate its advantages. Conclusion: The excellent and robust performances on three public databases proved that our method has the potential to be applied to CAD skin diseases diagnosis.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".