AWSnet: An Auto-weighted Supervision Attention Network for Myocardial Scar and Edema Segmentation in Multi-sequence Cardiac Magnetic Resonance Images. (arXiv:2201.05344v1 [eess.IV])

Multi-sequence cardiac magnetic resonance (CMR) provides essential pathology
information (scar and edema) to diagnose myocardial infarction. However,
automatic pathology segmentation can be challenging due to the difficulty of
effectively exploring the underlying information from the multi-sequence CMR
data. This paper aims to tackle the scar and edema segmentation from
multi-sequence CMR with a novel auto-weighted supervision framework, where the
interactions among different supervised layers are explored under a
task-specific objective using reinforcement learning. Furthermore, we design a
coarse-to-fine framework to …

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