Unsupervised Convolutional Filter Learning for COVID-19 Classification
Bibliographic record
Abstract
The outbreak of the SARS CoV-2, referred to as COVID-19, was initially reported in 2019 and has swiftly spread around the world. The identification of COVID-19 cases is one of the key factors to inhibit the spread of the virus. While there are multiple ways to diagnose COVID-19, these techniques are often expensive, time-consuming, or not readily available. Detection of COVID-19 using a radiological examination of Chest X-Rays provides a more viable, rapid, and efficient solution as it is easily available in most countries. The paper outlines a method that employs an unsupervised convolutional filter learning using Convolutional Autoencoder (CAE) followed by applying it to COVID-19 classification as a downstream task. This shows that the proposed technique provides state-of-the-art results with an average accuracy of 99.7%, AUC of 99.7%, specificity of 99.8%, sensitivity of 99.6%, and F1-score of 99.6%. We release the data and code for this work to aid further research.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".