Multiple disjoint dictionaries for representation of histopathology images
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
With the availability of whole-slide imaging in pathology, high-resolution images offer a more convenient disease observation but also require content-based retrieval of large scans. The bag-of-visual-words methodology has shown a high ability to describe the image content for recognition and retrieval purposes. In this work, a variant of the bag-of-visual-words with multiple dictionaries for histopathology image classification is proposed and tested on the image dataset Kimia Path24 with more than 27,000 patches of size 1000 × 1000 belonging to 24 different classes. Features are extracted from patches and clustered to form multiple codebooks. The histogram intersection approach and support vector machines are exploited to build multiple classifiers. At last, the majority voting determines the final classification for each patch. The experiments demonstrate the superiority of the proposed method for histopathology images that surpasses deep networks, LBP and other BoW results.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| 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