Self-Supervised Learning for Drug Discovery Using Nematode Images: Method and Dataset
Bibliographic record
Abstract
Parasitic worms are significant causes of human and livestock disease. The battle against infections caused by parasitic worms involves the exploration of numerous potential drug candidates. One approach in screening for new drug candidates is using natural product extracts on the nematode C. elegans as a model organism. A critical step in this process is the examination of microscopy images of C. elegans after exposure to natural product extracts. Automatic image classification accelerates the analysis process compared to purely visual identification by an expert. We report a new C. elegans image dataset including 12 717 microscopy images corresponding to natural product extracts, with about one-third of the images labeled by an expert and the remaining unlabeled. We make this dataset available to researchers for further development. We also propose a two-stage Semi-supervised Mix-up Barlow Twins Nematode Classifier (MBT-NC) to solve three image classification tasks involving nematode phenotypes after exposure to the studied natural extracts. MBT-NC combines self-supervised learning (SSL) for the feature representation stage (MBT) with a supervised classification stage (NC). In MBT, we utilize augmented and linearly interpolated samples for information maximization. Our method outperforms fully supervised and also other self-supervised methods on all three classification tasks: For binary, six-class, and 27-class classification, we outperform by 3.2%, 1.0%, and 2.2% respectively on test accuracy compared to the other methods. This is a new line of research in computer vision applications in healthcare.
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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 | 0.003 | 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".