Background　and　objective:　Modern　deep　neural　networks　are　highly　over-parameterized,　necessitating　the　use　of　data　augmentation　techniques　to　prevent　overfitting　and　enhance　generalization.　Generative　adversarial　networks　(GANs)　are　popular　for　synthesizing　visually　realistic　images.　However,　these　synthetic　images　often　lack　diversity　and　may　have　ambiguous　class　labels.　Recent　data　mixing　strategies　address　some　of　these　issues　by　mixing　image　labels　based　on　salient　regions.　Since　the　main　diagnostic　information　is　not　always　contained　within　the　salient　regions,　we　aim　to　address　the　resulting　label　mismatches　in　medical　image　classifications.　Methods:　We　propose　a　variety-guided　data　mixing　framework　(VariMix),　which　exploits　an　absolute　difference　map　(ADM)　to　address　the　label　mismatch　problems　of　mixed　medical　images.　VariMix　generates　ADM　using　the　image-to-image　(I2I)　GAN　across　multiple　classes　and　allows　for　bidirectional　mixing　operations　between　the　training　samples.　Results:　The　proposed　VariMix　achieves　the　highest　accuracy　of　99.30%　and　94.60%　with　a　SwinT　V2　classifier　on　a　Chest　X-ray　(CXR)　dataset　and　a　Retinal　dataset,　respectively.　It　also　achieves　the　highest　accuracy　of　87.73%,　99.28%,　95.13%,　and　95.81%　with　a　ConvNeXt　classifier　on　a　Breast　Ultrasound　(US)　dataset,　a　CXR　dataset,　a　Retinal　dataset,　and　a　Maternal-Fetal　US　dataset,　respectively.　Furthermore,　the　medical　expert　evaluation　on　generated　images　shows　the　great　potential　of　our　proposed　I2I　GAN　in　improving　the　accuracy　of　medical　image　classifications.　Conclusions:　Extensive　experiments　demonstrate　the　superiority　of　VariMix　compared　with　the　existing　GAN-　and　Mixup-based　methods　on　four　public　datasets　using　Swin　Transformer　V2　and　ConvNeXt　architectures.　Furthermore,　by　projecting　the　source　image　to　the　hyperplanes　of　the　classifiers,　the　proposed　I2I　GAN　can　generate　hyperplane　difference　maps　between　the　source　image　and　the　hyperplane　image,　demonstrating　its　ability　to　interpret　medical　image　classifications.　The　source　code　is　provided　in　https://github.com/yXiangXiong/VariMix.　©　2025　Elsevier　B.V.