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Nature's HorusEye Foundation Model Learns Medical Imaging Without Perfect Training Data
New self-supervised approach trains on noisy medical images across modalities, offering lessons for computer vision researchers
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HorusEye Foundation Model Learns Medical Imaging from Noisy X-Rays (No Perfect Data Required)
Stanford researchers built a foundation model that trains on messy hospital X-rays, proving clean datasets aren't always necessary for medical AI breakthroughs
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