Deep learning on compressed sensing measurements in pneumonia detection
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Pneumonia is one of the very common life‐threatening diseases and needs proper diagnosis at an early stage to be cured expeditiously. Medical practitioners use chest X‐ray as the best imaging modality to identify pneumonia. Due to the limited facilities available at the remote places and the need of maintaining the social distancing imposed by the recent outbreak of coronavirus disease, one may not have ease of access to a professional radiologist. This article proposes a deep learning (DL) framework that detects pneumonia from X‐ray images to assist the medical practitioners located at distant places. The X‐ray images are captured as compressed sensing (CS) measurements i.e. very few numbers of samples are observed in order to obtain an energy efficient and bandwidth preserving system to be utilized for far‐end pneumonia detection purpose. Extensive simulation results show that the proposed approach enables the detection of pneumonia with 96.48% accuracy when only 30% samples are transmitted.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 it