Stochastic Expectation Propagation Learning of Infinite Multivariate Beta Mixture Models for Human Tissue Analysis
Bibliographic record
Abstract
Nowadays, there is considerable and growing interest in applying accurate analysis tools to obtain meaningful information and extract knowledge from a huge amount of data. In this sense, unsupervised algorithms and clustering techniques have gained an increasing interest. These methods are helpful specifically when data annotation is time-consuming and costly. In this paper, we propose a new clustering method based on a Dirichlet process mixture of multivariate Beta distributions. To learn this novel Bayesian nonparametric model, we applied stochastic expectation propagation inference framework. This framework is able to define the model complexity and estimate the model’s parameters simultaneously. To demonstrate the efficiency of our model, we perform an experimental analysis using three real applications, breast, lung and colon histopathological tissue analysis. Our goal is to show that our algorithm could be considered as a machine learning framework in computer-assisted diagnosis and play the role of a complementary opinion to help the pathologists in making decisions with more accuracy.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".