1. Introduction Deep learning has ushered in an era of unprecedented advancements in computer vision, demonstrating remarkable capabilities in the analysis of natural images across a multitude of tasks 1. This success has naturally spurred interest in leveraging these powerful techniques within the domain of medical imaging, a field where the potential for enhanced diagnostics, personalized treatment strategies, and improved patient outcomes is immense 3. By training on extensive datasets of clinical images, deep learning models can learn to discern, categorize, and quantify intricate patterns that may be indicative of various diseases and medical conditions 3.
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