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The robust and fully automatic interpretation of multi-view echocardiographic sequences across multi-vendor and multi-center is a challenging task due to abounding artifacts, low signal-to-noise ratio, large shape variations among different views, and large gaps across different centers and vendors. In this paper, a dense pyramid and deep supervision network (DPSN) is proposed to tackle this challenging task. DPSN incorporates the advantages of the densely connected network, feature pyramid network, and deeply supervised network, which help to extract and fuse multi-level and multi-scale holistic semantic information. This capability endows DPSN with prominent generalization and robustness, enabling it to yield a precise interpretation. To reduce the computational complexity and avoid the frequent information loss in temporal modeling, DPSN processes all frames independently (i.e., without utilizing …