Uveal Pigmented Lesion Classification, Detection, and Segmentation: A Comparative Analysis of Machine Learning Tasks.

Purpose: Uveal melanoma (UM) is the most common intraocular malignancy in adults, with high metastatic risk and poor prognosis. Current screening and triaging methods for melanocytic choroidal tumors face inherent limitations, particularly in regions with limited access to specialized ocular oncologists. This study proposes a reference framework for future research in UM detection. It highlights the trade-offs between different computer vision (CV) approaches based on data availability, computer resources, annotation effort, and clinical applicability. Methods: In total, 864 Optos images of UM, choroidal nevi, and congenital hypertrophy of the retinal pigment epithelium were included in the study. Three CV models-classification, detection, and segmentation-were implemented using a shared ResNet-50 backbone. Performance metrics included area under the curve (AUC) for classification, F1 score for detection, and Dice score for segmentation. An ablation study evaluated robustness to data scarcity. Model interpretability was enhanced through Grad-CAM visualizations and confusion matrices. Results: Classification, detection, and segmentation achieved AUC scores of 94%, 93%, and 95%, respectively. Segmentation showed the best F1 score (89%) and a Dice score of 75%. Classification performed the best with limited data. Conclusions: In high-resource scenarios (100+ images), all models performed similarly, while in low-resource scenarios (<70 images), the classification model outperformed the others. This suggests that simpler models may offer better value for classification tasks with limited resources. Translational Relevance: Given different CV algorithms' different clinical use cases and development costs, this study takes a comparative look at these algorithms for the task of UM analysis on ultra-widefield fundus images.