Glaucoma　is　one　of　the　causes　that　leads　to　irreversible　vision　loss.　Automatic　glaucoma　detection　based　on　fundus　images　has　been　widely　studied　in　recent　years.　However,　existing　methods　mainly　depend　on　a　considerable　amount　of　labeled　data　to　train　the　model,　which　is　a　serious　constraint　for　real-world　glaucoma　detection.In　this　paper,　we　introduce　a　transfer　learning　technique　that　leverages　the　fundus　feature　learned　from　similar　ophthalmic　data　to　facilitate　diagnosing　glaucoma.　Specifically,　a　Transfer　Induced　Attention　Network　(TIA-Net)　for　automatic　glaucoma　detection　is　proposed,　which　extracts　the　discriminative　features　that　fully　characterize　the　glaucoma-related　deep　patterns　under　limited　supervision.　By　integrating　the　channel-wise　attention　and　maximum　mean　discrepancy,　our　proposed　method　can　achieve　a　smooth　transition　between　general　and　specific　features,　thus　enhancing　the　feature　transferability.To　delimit　the　boundary　between　general　and　specific　features　precisely,　we　first　investigate　how　many　layers　should　be　transferred　during　training　with　the　source　dataset　network.　Next,　we　compare　our　proposed　model　to　previously　mentioned　methods　and　analyze　their　performance.　Finally,　with　the　advantages　of　the　model　design,　we　provide　a　transparent　and　interpretable　transferring　visualization　by　highlighting　the　key　specific　features　in　each　fundus　image.　We　evaluate　the　effectiveness　of　TIA-Net　on　two　real　clinical　datasets　and　achieve　an　accuracy　of　85.7%/76.6%,　sensitivity　of　84.9%/75.3%,　specificity　of　86.9%/77.2%,　and　AUC　of　0.929　and　0.835,　far　better　than　other　state-of-the-art　methods.Different　from　previous　studies　applied　classic　CNN　models　to　transfer　features　from　the　non-medical　dataset,　we　leverage　knowledge　from　the　similar　ophthalmic　dataset　and　propose　an　attention-based　deep　transfer　learning　model　for　the　glaucoma　diagnosis　task.　Extensive　experiments　on　two　real　clinical　datasets　show　that　our　TIA-Net　outperforms　other　state-of-the-art　methods,　and　meanwhile,　it　has　certain　medical　value　and　significance　for　the　early　diagnosis　of　other　medical　tasks.