Medical Open Set Recognition via Intra-Class Clustering
Bibliographic record
Abstract
In computational medical imaging, model's ability to identify whether a sample is from an unseen semantic category is critical in clinical deployments. However, conventional medical image recognition usually assumes a closed-set setting where all queries in testing are from pre-defined training categories and overlooks the fact that in practice it is possible to have queries from unknown categories such as unknown or unseen tissue. In this study, we particularly tackle this thorny challenge, namely Medical Open Set Recognition (MOSR), and explore it on medical image classification and diagnosis. The biggest challenge with this issue lies in deep model's overconfidence due to relatively large intra-class variance, which leads to incorrectly assigning an unknown sample to a known class with a high confidence level. To address this problem, we introduce intra-class clustering, which divides the samples assigned to each class into several low-variance sub-clusters. In addition, we propose to divide the samples uniformly to each cluster by optimal transport to achieve online clustering. Extensive experiments on 6 public medical imaging datasets demonstrate that a classification model trained with the proposed intra-class clustering dramatically alleviate the overconfidence problem with competitive accuracy and thus effective for improving MOSR performance. Our benchmarks and code will be publicly released when published.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".