Soferi_mix < UHD 2025 >

: Instead of hard-swapping patches, SoftMix applies a transition mask that blends the features of both source images at the edges of the patch.

Data scarcity and class imbalance are significant hurdles in medical image-based diagnosis. While traditional Data Augmentation (DA) and Generative Adversarial Networks (GANs) have been used, patch-based methods like provide a more nuanced approach. This paper investigates SoftMix's ability to augment patched medical images, improving the robustness and accuracy of deep learning classification models. 1. Introduction soferi_mix

SoftMix operates on the principle of from different images to create a composite training sample. Unlike traditional "Mixup" (which blends images pixel-wise) or "CutMix" (which replaces a hard rectangular patch), SoftMix utilizes a "softer" approach to blending boundaries. Selection : Two images from the training set are selected. Patching : The images are divided into discrete patches. : Instead of hard-swapping patches, SoftMix applies a

SoftMix: A Novel Data Augmentation for Patched Medical Image Classification AI Demystified: Medical Imaging Accuracy Systematic Review of Data-Centric AI This paper investigates SoftMix's ability to augment patched