The　heterogeneity　of　medical　images　poses　significant　challenges　to　accurate　disease　diagnosis.　To　tackle　this　issue,　the　impact　of　such　heterogeneity　on　the　causal　relationship　between　image　features　and　diagnostic　labels　should　be　incorporated　into　model　design,　which　however　remains　underexplored.　In　this　paper,　we　propose　a　mixed　prototype　correction　for　causal　inference　(MPCCI)　method,　aimed　at　mitigating　the　impact　of　unseen　confounding　factors　on　the　causal　relationships　between　medical　images　and　disease　labels,　so　as　to　enhance　the　diagnostic　accuracy　of　deep　learning　models.　The　MPCCI　comprises　a　causal　inference　component　based　on　front-door　adjustment　and　an　adaptive　training　strategy.　The　causal　inference　component　employs　a　multi-view　feature　extraction　(MVFE)　module　to　establish　mediators,　and　a　mixed　prototype　correction　(MPC)　module　to　execute　causal　interventions.　Moreover,　the　adaptive　training　strategy　incorporates　both　information　purity　and　maturity　metrics　to　maintain　stable　model　training.　Experimental　evaluations　on　four　medical　image　datasets,　encompassing　CT　and　ultrasound　modalities,　demonstrate　the　superior　diagnostic　accuracy　and　reliability　of　the　proposed　MPCCI.　The　code　will　be　available　at　https://github.com/Yajie-Zhang/MPCCI.　©　2024　Owner/Author.