Report-writing　for　Brain　Computed　Tomography　(CT)　imaging　is　a　routine　procedure　for　diagnosing　cerebrovascular　diseases,　while　it　is　time-consuming　and　tedious　for　radiologists　especially　in　highly　populated　areas.　Automatic　report　generation　has　the　potential　to　alleviate　radiologists＇　workload　and　reduce　the　diagnose　error.　Currently,　the　development　of　image　captioning　and　medical　image　processing　has　driven　great　achievements　in　medical　report　generation.　However,　there　is　no　report　generation　study　for　the　Brain　CT　imaging　and　this　task　faces　the　following　challenges:　First,　Brain　CT　lesions　are　disperse　in　3-D　space,　with　more　morphological　instability.　Second,　the　Brain　CT　reports　are　long　paragraphs　with　similar　medical　term.　These　challenges　increase　the　difficulty　o　f　lesions　recognition　and　report　generation　for　Brain　CT　imaging.　To　cope　with　these　challenges,　we　propose　a　weakly　guided　hierarchical　encoder-decoder　network　for　lesions　learning　and　Brain　CT　report　generation.　Specifically,　we　propose　a　weakly　guided　attention　model　(WGAM)　in　encoder　to　capture　the　most　important　areas　and　scans　gradually　under　the　weak　guidance　of　possible　lesions　areas.　In　addition,　we　propose　a　keywords-driven　interactive　recurrent　network　(KIRN)　in　decoder　to　generate　paragraphs　under　the　weak　guidance　of　possible　lesions　keywords.　Experiments　on　our　Brain　CT　dataset　demonstrate　the　effectiveness　of　the　proposed　method.　©　2021　IEEE.