Diabetic　retinopathy　(DR)　is　one　of　the　major　causes　of　blindness.　It　is　of　great　significance　to　apply　deep-learning　techniques　for　DR　recognition.　However,　deep-learning　algorithms　often　depend　on　large　amounts　of　labeled　data,　which　is　expensive　and　time-consuming　to　obtain　in　the　medical　imaging　area.　In　addition,　the　DR　features　are　inconspicuous　and　spread　out　over　high-resolution　fundus　images.　Therefore,　it　is　a　big　challenge　to　learn　the　distribution　of　such　DR　features.　This　article　proposes　a　multichannel-based　generative　adversarial　network　(MGAN)　with　semisupervision　to　grade　DR.　The　multichannel　generative　model　is　developed　to　generate　a　series　of　subfundus　images　corresponding　to　the　scattering　DR　features.　By　minimizing　the　dependence　on　labeled　data,　the　proposed　semisupervised　MGAN　can　identify　the　inconspicuous　lesion　features　by　using　high-resolution　fundus　images　without　compression.　Experimental　results　on　the　public　Messidor　data　set　show　that　the　proposed　model　can　grade　DR　effectively.　Note　to　Practitioners-This　article　is　motivated　by　the　challenging　problem　due　to　the　inadequacy　of　labeled　data　in　medical　image　analysis　and　the　dispersion　of　efficient　features　in　high-resolution　medical　images.　As　for　the　inadequacy　of　labeled　data　in　medical　image　analysis,　the　reasons　mainly　include　the　followings:　1)　the　high-quality　annotation　of　medical　imaging　sample　depends　heavily　on　scarce　medical　expertise　which　is　very　expensive　and　2)　comparing　with　natural　issues,　it　is　more　difficult　to　collect　medical　images　because　of　privacy　issues.　It　is　of　great　significance　to　apply　deep-learning　techniques　for　diabetic　retinopathy　(DR)　recognition.　In　this　article,　the　multichannel　generative　adversarial　network　(GAN)　with　semisupervision　is　developed　for　DR-aided　diagnosis.　The　proposed　model　can　deal　with　DR　classification　problem　with　inadequacy　of　labeled　data　in　the　following　ways:　1)　the　multichannel　generative　scheme　is　proposed　to　generate　a　series　of　subfundus　images　corresponding　to　the　scattering　DR　features　and　2)　the　proposed　multichannel-based　GAN　(MGAN)　model　with　semisupervision　can　make　full　use　of　both　labeled　data　and　unlabeled　data.　The　experimental　results　demonstrate　that　the　proposed　model　outperforms　the　other　representative　models　in　terms　of　accuracy,　area　under　ROC　curve　(AUC),　sensitivity,　and　specificity.