: Instead of hard-swapping patches, SoftMix applies a transition mask that blends the features of both source images at the edges of the patch.
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. soferi_mix
Recent reviews of over 100 medical image augmentation papers indicate that methods like SoftMix significantly reduce in small datasets. In patched classification tasks—such as identifying malignant vs. benign tissue—SoftMix helps the model learn more generalized features by preventing it from relying on sharp, artificial edges created by other mixing techniques. 5. Conclusion : Instead of hard-swapping patches, SoftMix applies a