Automated analysis of vascular structures of skin lesions : segmentation, pattern recognition and computer-aided diagnosis
Bibliographic record
Abstract
Skin disorders are among the most common healthcare referrals in Canada, affecting a large population and imposing high healthcare costs. Early detection plays an essential role in efficient management and better outcome. However, restricted access to dermatologists and lack of education to other healthcare professionals pose a major challenge for early detection. Computer-aided systems have great potential as viable tools to identify early skin abnormalities. The initial clinical diagnosis phase of most skin disorders involves visual inspection of the lesion for specific features, associated with certain abnormalities. One of the main cutaneous features are vascular structures, which are significantly involved in pathogenesis, diagnosis, and treatment outcome of skin abnormalities. The presence and morphology of cutaneous vessels are suggestive clues for specific abnormalities. However, there has been no systematic approach for comprehensive analysis of skin vasculature. In this thesis, we propose a three-level framework to systematically detect, quantify and analyze the characteristics of superficial cutaneous blood vessels. First, we investigate the vessels at pixel-level. We propose novel techniques for detection (absence/presence) and segmentation of vascular structures in pigmented and non-pigmented lesions and evaluate the performance quantitatively. We develop a fully automatic vessel segmentation framework based on decomposing the skin into its component chromophores and accounting for shape. Furthermore, we design a deep learning framework based on stacked sparse auto-encoders for detection and localization of skin vasculature. Compared to previous studies, we achieve higher detection performance, while preserving clinical feature interpretability. Next, we analyze the vessels at lesion-level. We propose a novel set of architectural, geometrical and topological features to differentiate vascular morphologies. The defined feature set can effectively differentiate four major classes of vascular patterns. Finally, we investigate the vessels at disease-level. We analyze the relationship between vascular characteristics and disease diagnosis. We design and deploy novel features to evaluate total blood content and vascular characteristics of the lesion to differentiate cancerous lesions from benign ones. We also build a system upon integrating patient’s clinical information and lesion’s visual characteristics using deep feature learning, which achieves superior cancer classification performance compared to current techniques without the need for handcrafted high-level features.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".