Image Retrieval-Based Decision Support System for Dermatoscopic Images
Bibliographic record
Abstract
This paper presents a content-based image retrieval system for dermatoscopic images as a diagnostic aid to the dermatologists for skin cancer recognition. In this context, the ultimate aim is to support decision making by locating, retrieving and displaying relevant past cases along with diagnostic reports. However, most challenging aspect in this domain is to extract local lesion specific image features and define the relevance between query and database images for retrieval. A fast and automatic segmentation method to detect the lesion from background healthy skin is proposed. This method first transforms a color image into an intensity image by utilizing domain specific image properties and NBS color distance in HVC color space. Lesion mask is detected by fusing individually segmented images based on iterative thresholding. Lesion specific local color and texture features are extracted and represented in the form of mean and variance-covariance of color channels and in a reduced PCA sub-space. Finally, for effective image retrieval, a similarity matching function is defined based on the fusion of a Bhattacharyya and Euclidean distance metric. The performance of the retrieval system is evaluated using average precision on a collection of 358 images, which demonstrates effectiveness of the proposed approach
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.005 | 0.003 |
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".