Automatic　generation　of　medical　reports　for　Brain　Computed　Tomography　(CT)　imaging　is　crucial　for　helping　radiologists　make　more　accurate　clinical　diagnoses　efficiently.　Brain　CT　imaging　typically　contains　rich　pathological　information,　including　common　pathologies　that　often　co-occur　in　one　report　and　rare　pathologies　that　appear　in　medical　reports　with　lower　frequency.　However,　current　research　ignores　the　potential　co-occurrence　between　common　pathologies　and　pays　insufficient　attention　to　rare　pathologies,　severely　restricting　the　accuracy　and　diversity　of　the　generated　medical　reports.　In　this　paper,　we　propose　a　Co-occurrence　Relationship　Driven　Hierarchical　Attention　Network　(CRHAN)　to　improve　Brain　CT　report　generation　by　mining　common　and　rare　pathologies　in　Brain　CT　imaging.　Specifically,　the　proposed　CRHAN　follows　a　general　encoder-decoder　framework　with　two　novel　attention　modules.　In　the　encoder,　a　co-occurrence　relationship　guided　semantic　attention　(CRSA)　module　is　proposed　to　extract　the　critical　semantic　features　by　embedding　the　co-occurrence　relationship　of　common　pathologies　into　semantic　attention.　In　the　decoder,　a　common-rare　topic　driven　visual　attention　(CRVA)　module　is　proposed　to　fuse　the　common　and　rare　semantic　features　as　sentence　topic　vectors,　and　then　guide　the　visual　attention　to　capture　important　lesion　features　for　medical　report　generation.　Experiments　on　the　Brain　CT　dataset　demonstrate　the　effectiveness　of　the　proposed　method.　IEEE