Accurate　segmentation　of　myocardial　pathology,　including　scar　and　edema,　from　magnetic　resonance　images　(MRI)　is　of　significant　clinical　importance　for　diagnosing,　planning　treatment,　and　assessing　prognosis　in　cardiovascular　diseases.　Multi-sequence　cardiac　magnetic　resonance　images　provide　additional　information　for　pathological　segmentation,　while　ventricular　blood　pool　regions　offer　positional　anatomical　priors　for　identifying　scar　and　edema.　Therefore,　we　propose　a　multi-task　framework　based　on　SimAM-UNet,　where　SimAM　generates　feature　weights　to　better　focus　on　modality-specific　features　for　the　segmentation　of　myocardial　pathology　using　multi-sequence　cardiac　magnetic　resonance　images.　The　first　task　involves　segmenting　and　extracting　features　of　the　myocardium　and　blood　pool,　which　are　relatively　easy　to　obtain.　The　second　task　focuses　on　segmenting　scars　and　edema　by　integrating　positional　feature　constraints　derived　from　the　myocardium　and　blood　pool.　To　address　the　multi-center　data　problem　encountered　in　real-world　data,　we　incorporate　a　data　augmentation　pipeline　that　simulates　the　generation　of　data　samples　from　different　centers,　enhancing　the　domain　generalization　of　our　method.　Our　proposed　method　was　evaluated　on　the　MyoPS++　track　of　the　CARE2024　Challenge,　and　the　experimental　results　demonstrated　the　effectiveness　of　our　multi-task　framework　for　myocardial　pathological　segmentation.　©　The　Author(s),　under　exclusive　license　to　Springer　Nature　Switzerland　AG　2025.