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Deep learning models for medical imaging require massive training datasets to achieve high accuracy. However, gathering labeled medical data is costly and ethically complex. Data augmentation—the process of creating "new" samples from existing ones—is the primary solution. has emerged as a specialized technique to address the unique structural features of medical images, such as tumors or lesions, which are often analyzed in patches rather than whole-slide images. 2. Methodology

SoftMix represents a critical advancement in data-centric AI for healthcare. By softening the boundaries between mixed image patches, it provides a more realistic and effective training signal for medical diagnosis models. Future research should focus on its application in real-time surgical imaging and rarer pathology detection where data is most limited. soferi_mix

SoftMix: A Novel Data Augmentation for Patched Medical Image Classification AI Demystified: Medical Imaging Accuracy Systematic Review of Data-Centric AI Deep learning models for medical imaging require massive