Deep　neural　networks　(DNNs)　have　been　widely　applied　in　medical　image　classification　and　achieve　remarkable　classification　performance.　These　achievements　heavily　depend　on　large-scale　accurately　annotated　training　data.　However,　label　noise　is　inevitably　introduced　in　the　medical　image　annotation,　as　the　labeling　process　heavily　relies　on　the　expertise　and　experience　of　annotators.　Meanwhile,　DNNs　suffer　from　overfitting　noisy　labels,　degrading　the　performance　of　models.　Therefore,　in　this　work,　we　innovatively　devise　a　noise-robust　training　approach　to　mitigate　the　adverse　effects　of　noisy　labels　in　medical　image　classification.　Specifically,　we　incorporate　contrastive　learning　and　intra-group　mixup　attention　strategies　into　vanilla　supervised　learning.　The　contrastive　learning　for　feature　extractor　helps　to　enhance　visual　representation　of　DNNs.　The　intra-group　mixup　attention　module　constructs　groups　and　assigns　self-attention　weights　for　group-wise　samples,　and　subsequently　interpolates　massive　noisy-suppressed　samples　through　weighted　mixup　operation.　We　conduct　comparative　experiments　on　both　synthetic　and　real-world　noisy　medical　datasets　under　various　noise　levels.　Rigorous　experiments　validate　that　our　noise-robust　method　with　contrastive　learning　and　mixup　attention　can　effectively　handle　with　label　noise,　and　is　superior　to　state-of-the-art　methods.　An　ablation　study　also　shows　that　both　components　contribute　to　boost　model　performance.　The　proposed　method　demonstrates　its　capability　of　curb　label　noise　and　has　certain　potential　toward　real-world　clinic　applications.