The　scarcity　of　training　annotation　is　one　of　the　major　challenges　for　the　application　of　deep　learning　technology　in　medical　image　analysis.　Recently,　self-supervised　learning　provides　a　powerful　solution　to　alleviate　this　challenge　by　extracting　useful　features　from　a　large　number　of　unlabeled　training　data.　In　this　article,　we　propose　a　simple　and　effective　self-supervised　learning　method　for　leukocyte　classification　by　identifying　the　different　transformations　of　leukocyte　images,　without　requiring　a　large　batch　of　negative　sampling　or　specialized　architectures.　Specifically,　a　convolutional　neural　network　backbone　takes　different　transformations　of　leukocyte　image　as　input　for　feature　extraction.　Then,　a　pretext　task　of　self-supervised　transformation　recognition　on　the　extracted　feature　is　conducted　by　a　classifier,　which　helps　the　backbone　learn　useful　representations　that　generalize　well　across　different　leukocyte　types　and　datasets.　In　the　experiment,　we　systematically　study　the　effect　of　different　transformation　compositions　on　useful　leukocyte　feature　extraction.　Compared　with　five　typical　baselines　of　self-supervised　image　classification,　experimental　results　demonstrate　that　our　method　performs　better　in　different　evaluation　protocols　including　linear　evaluation,　domain　transfer,　and　finetuning,　which　proves　the　effectiveness　of　the　proposed　method.