Learning Similarity via Subjective Evaluations and Deep Features of Histopathology Images
Bibliographic record
Abstract
Visual similarity estimation for histopathology images plays a key role in many medical imaging tasks, especially in image search and retrieval. All image similarity evaluation approaches employ distance-based metrics to quantify the degree of (dis) similarity. However, it has always been challenging to numerically estimate the similarity between two images, which is compatible with subjective assessment of the human operator, i.e., physicians such as radiologists and pathologists. Relying only on distance calculations through Euclidean, Manhattan, Hamming, and cosine distances does not provide us with the result that can be translated to human judgment in linguistic terms and/or in a normalized range. There is a need for a reliable image similarity measurement compatible with the human assessment with minimum possible conflict. This work proposes a new scheme that evaluates the similarity between a pair of histopathology images close to human reasoning using a fuzzy-logic approach. To this end, we developed a web application to interface with users and to collect descriptive image similarity data for training and testing purposes. We designed an adaptive neuro-fuzzy inference system (ANFIS) to model the vague and uncertain nature of user image assessment for the histopathology image comparison task. The experimental results show that the trained ANFIS can estimate the image similarity with acceptable accuracy and consistent with the user evaluations.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".