Pre-training on modality-matched data significantly improves downstream performance in medical imaging models while self-supervised learning benefits depend on context.
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MAE-SAM2 integrates MAE self-supervised learning with SAM2 to achieve superior segmentation of retinal vascular leakage on fluorescein angiography images, with highest Dice/IoU scores and 5% improvement over original SAM2.
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From pre-training to downstream performance: Does domain-specific pre-training make sense?
Pre-training on modality-matched data significantly improves downstream performance in medical imaging models while self-supervised learning benefits depend on context.
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MAE-SAM2: Mask Autoencoder-Enhanced SAM2 for Clinical Retinal Vascular Leakage Segmentation
MAE-SAM2 integrates MAE self-supervised learning with SAM2 to achieve superior segmentation of retinal vascular leakage on fluorescein angiography images, with highest Dice/IoU scores and 5% improvement over original SAM2.