Choroidal Nevi Classification in Fundus Images Using a Patch-Based Deep Learning Approach
Bibliographic record
Abstract
Choroidal nevi are difficult to identify and require regular eye screening. While high-resolution fundus images are commonly used to identify choroidal nevi, manual review is time-consuming and requires specialized knowledge. Deep learning shows promise for accurate classification of eye diseases. However, these models require extensive labelled data, which is challenging for some medical conditions, like choroidal nevi. The objective of this study is to use a patch-based approach to classify fundus images as having choroidal nevus or not. We address data limitations, and the challenges posed by high-resolution images, through two key strategies. First, we uniformly extract tiled patches, expanding the training dataset, allowing a more detailed analysis of lesions, and preserving image quality by avoiding downsampling. However, this introduces a class imbalance issue which we effectively address through a second strategy involving different augmentations applied to the underrepresented class, mitigating the class imbalance issue, and further increasing the training set size. This approach achieved 92.61% accuracy, 90.47% recall, and 93.82% precision, outperforming the model trained on full-size images. We conclude that using a patch-based approach with noise and contrast enhancements outperforms conventional and simpler patch-based models.Clinical Relevance: This research tests a method for automating choroidal nevi identification to inform clinical diagnosis.
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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 | 0.000 | 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 | 0.000 | 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".