Initial　segmentation　of　key　parts　of　a　medical　image　is　a　critical　step　for　computers　to　help　doctors　analyze　medical　images.　Fully　supervised　deep　learning　segmentation　models　rely　on　large-scale　pixel-level　labeling;　however,　the　number　of　medical　images　with　pixel-level　labels　is　small　due　to　the　high　cost　of　annotation.　Therefore,　this　paper　proposes　a　weakly　supervised　medical　image　segmentation　method　that　requires　only　image-level　label.　The　method　uses　high-confidence　regions　of　class　activation　mapping　(CAM)　as　supervised　information　and　trains　a　generative　adversarial　network　with　added　cycle　consistency　loss　to　establish　the　mapping　relationship　between　the　original　image　and　the　segmentation　mask.　We　name　this　method　CAM-CycleGAN.　With　this　approach,　the　model　can　be　trained　to　acquire　subregions　that　are　highly　correlated　with　the　category　and　can　preserve　the　fine-grained　features　of　the　original　image.　Finally,　we　demonstrate　the　effectiveness　of　the　proposed　method　in　the　publicly　available　colon　gland　dataset　GlaS.　©　2024　IEEE.