Self-supervised Anomaly Detection with Random-shape Pseudo-outliers
Bibliographic record
Abstract
Anomaly detection in a medical image is a challenging yet essential task. It relies on learning patterns/distributions from health data only, and no abnormal samples are available during training. This study proposes a novel self-supervised learning method to precisely detect and localize anomalies in MRI medical images. We synthesize abnormal images by overlaying random pseudo-outliers onto normal samples and propose a discriminative model for anomaly detection. Unlike prior arts that generate abnormalities with pre-determined regular geometric shapes, we introduce a new outlier synthesis strategy capable of generating random-shape anomalies. By learning the disentanglement of pseudo-outliers and normal regions in the synthesized images, our model can capture natural anomalies in images at both the pixel level and sample level. We present our empirical experimentation on two publicly accessible datasets and demonstrate the proposed method's superiority over SOTA solutions on MRIs.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".