Biomedical　image　segmentation　is　an　essential　task　in　the　computer-aided　diagnosis　system.　An　encoder-decoder　based　on　a　shallow　or　deep　convolutional　neural　network　(DCNN)　is　an　extensively　used　framework　for　biomedical　image　analysis.　To　study　and　rethink　the　effectiveness　of　compounding　both　the　shallow　and　deep　networks　for　the　medical　image　segmentation　task,　we　propose　a　dual-model　CNN　architecture,　called　DM-Net,　for　biomedical　image　segmentation.　DM-Net　is　composed　of　a　shallow　CNN　structure　at　its　left,　called　L-Net　and　a　deeper　CNN　structure　at　its　right,　named　R-Net.　The　L-Net　is　proposed　to　encode　low-level　contextual　information　and　the　R-Net　is　presented　to　produce　high-level　semantic　feature　maps.　Furthermore,　a　novel　crossed-skip　connection　(CSC)　strategy　is　proposed　to　transfer　information　between　the　two　side　networks　mutually.　Extensive　experiments　demonstrate　that　our　method　outperforms　representative　approaches　on　three　public　medical　image　datasets.　©　2023,　The　Author(s),　under　exclusive　license　to　Springer　Nature　Switzerland　AG.