Supervised Machine Learning based Medical Image Annotation and Retrieval.
Bibliographic record
Abstract
This paper presents the approaches and experimental results of image annotation and retrieval in our first participation of ImageCLEFmed 2005. In this work, we investigate a supervised learning approach to associate low-level global image features with their high level visual and/or semantic categories for image annotation and retrieval. For automatic image annotation, we represent input images through a large dimensional feature vector of texture, edge and shape features. A multi-class classification system based on pairwise coupling of several binary support vector machine (SVM) is trained on this input to predict the categories of test images, which will be effective for later annotation. For visual only retrieval, we utilize a low dimensional feature vector of color, texture and edge features based on principal component analysis (PCA) and category specific feature distribution information in a statistical similarity measure function. Based on the online category prediction of query and database images by the multi-class SVM classifier, pre-computed category specific first and second order statistical parameters are utilized in Bhattacharyya distance measure on the assumption that distributions are multivariate Gaussian. Experimental results of both image annotation and retrieval are reported in this paper.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".