Public articles linked to the same research event.
medRxiv Using choroid segmentation as a prototype task on 6,076 OCT B-scans from 80 subjects, this study systematically compared scalar no-reference image quality metrics (BRISQUE, NIQE, PIQE, SNR, PSNR), general-purpose ImageNet-pretrained representations, and retinal foundation models as task-specific quality gates, finding that scalar metrics correlate weakly with segmentation Dice (|r| < 0.20), that general-purpose pretrained representations reach linear-probe ROC-AUC up to about 0.77, that the OCT-specific foundation model RETFound reaches about 0.81, and that only the retinal foundation model embeddings form quality-aligned unsupervised K-Means clusters exceeding a patient-level permutation null.
Using choroid segmentation as a prototype task on 6,076 OCT B-scans from 80 subjects, this study systematically compared scalar no-reference image quality metrics (BRISQUE, NIQE, PIQE, SNR, PSNR), general-purpose ImageNet-pretrained representations, and retinal foundation models as task-specific quality gates, finding that scalar metrics correlate weakly with segmentation Dice (|r| < 0.20), that general-purpose pretrained representations reach linear-probe ROC-AUC up to about 0.77, that the OCT-specific foundation model RETFound reaches about 0.81, and that only the retinal foundation model embeddings form quality-aligned unsupervised K-Means clusters exceeding a patient-level permutation null.
Using choroid segmentation as a prototype task on 6,076 OCT B-scans from 80 subjects, this study systematically compared scalar no-reference image quality metrics (BRISQUE, NIQE, PIQE, SNR, PSNR), general-purpose ImageNet-pretrained representations, and retinal foundation models as task-specific quality gates, finding that scalar metrics correlate weakly with segmentation Dice (|r| < 0.20), that general-purpose pretrained representations reach linear-probe ROC-AUC up to about 0.77, that the OCT-specific foundation model RETFound reaches about 0.81, and that only the retinal foundation model embeddings form quality-aligned unsupervised K-Means clusters exceeding a patient-level permutation null.
Using choroid segmentation as a prototype task on 6,076 OCT B-scans from 80 subjects, this study systematically compared scalar no-reference image quality metrics (BRISQUE, NIQE, PIQE, SNR, PSNR), general-purpose ImageNet-pretrained representations, and retinal foundation models as task-specific quality gates, finding that scalar metrics correlate weakly with segmentation Dice (|r| < 0.20), that general-purpose pretrained representations reach linear-probe ROC-AUC up to about 0.77, that the OCT-specific foundation model RETFound reaches about 0.81, and that only the retinal foundation model embeddings form quality-aligned unsupervised K-Means clusters exceeding a patient-level permutation null.