New method to improve the diagnostic utility of OCTA images in retinal disease
Bibliographic record
Abstract
Purpose: \nDiagnosing medical images necessitates years of experience to ensure accurate diagnoses. However, the current workforce available for this task falls significantly short compared to the volume of images requiring assessment. This places a considerable burden on the medical system during diagnosis. Additionally, medical images often contain artifacts, further complicating and prolonging the diagnostic process. This thesis serves as a solution to expedite diagnosis by enhancing the image quality of Optical Coherence Tomography Angiography (OCTA) images, thereby alleviating the strain on the system. \n \nAims: \n1. Method 1 (Chapter 2): Removal of motion artifacts from OCTA images. It is one of the toughest artifacts to be removed from an image. \n2. Method 2 (Chapter 3): Super-Resolution of OCTA image. Increasing the dimensions of the image and enhancing the quality to make diagnosis process efficient. \n \nConclusion: This work allows the removal of motion artifacts from the OCTA image and then enhance the quality of the image using super-resolution. In chapter 4 we show that the scatterplots were used to compare the correlations of the most commonly used parameters, Foveal Avascular Zone (FAZ) area, perimeter, and circularity index, between before and after super-resolution at ×2 and ×3 magnification. A p-value < 0.05 was considered significant for all statistical tests. Thus, making the diagnosis process simpler and better for medical practitioners.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".