Brain　CT　is　the　first　choice　for　diagnosing　intracranial　diseases.　However,　the　doctors　who　can　accurate　diagnosis　is　insufficient　with　the　increasing　number　of　patients.　Nowadays,　many　computer-aided　diagnosis　algorithms　were　developed　to　help　doctors　diagnose　and　reduce　time.　However,　most　of　the　research　classifies　each　slice　isolated,　regard　this　as　an　image-level　classification　problem.　It　is　not　comprehensive　enough　because　many　conditions　can　only　be　diagnosed　by　considering　adjacent　slices　and　the　relationships　between　diseases.　In　order　to　better　fit　the　characteristics　of　this　task,　we　formal　it　as　a　Multi-instance　Multi-label　(MIML)　learning　problem　at　sequence-level.　In　this　paper,　we　analyze　the　difficulties　in　the　brain　CT　images　classification　domain　And　we　propose　an　efficient　model　that　can　improve　performance,　reduce　the　number　of　parameters　and　give　model　explanations.　In　our　model,　the　convolution　neural　network　(CNN)　extracts　the　feature　vector　from　each　image　of　a　set　of　full　slice　brain　CT.　The　multi-instance　detect　module　focuses　on　the　key　images　which　could　assist　doctors　quickly　locate　suspicious　images　and　avoid　mistakes.　We　evaluated　our　model　on　two　datasets:　CQ500　and　RSNA.　The　Fl　scores　are　0.897　and　0.854　respectively.　The　proposed　model　outperforms　the　previous　sequence-level　model　SDLM　with　only　a　quarter　of　the　parameters.　Low　computation　and　high　performance　make　the　model　have　clinical　applicability.　Copyright　(C)　2020　The　Authors.