The Encoder-Decoder architecture is a main stream deep learning model for biomedical image segmentation. The encoder fully compresses the input and generates encoded features, and the decoder then produces dense predictions using encoded features. However, decoders are still under-explored in such architectures. In this paper, we comprehensively study the state-of-the-art Encoder-Decoder architectures, and propose a new universal decoder, called cascade decoder, to improve semantic segmentation accuracy. Our cascade decoder can be embedded into existing networks and trained altogether in an end-to-end fashion. The cascade decoder structure aims to conduct more effective decoding of hierarchically encoded features and is more compatible with common encoders than the known decoders. We replace the decoders of state-of-the-art models with our cascade decoder for several challenging biomedical image segmentation tasks, and the considerable improvements achieved demonstrate the efficacy of our new decoding method.
翻译:编码器- Decoder 结构是生物医学图像分割的主要流深学习模型。 编码器充分压缩输入并生成编码特性, 编码器随后使用编码特性生成密集的预测。 但是, 解码器在这种结构中仍然未得到充分开发。 在本文中, 我们全面研究最先进的编码器- Decoder 结构, 并提议一个新的通用解码器, 称为级联解码器, 以提高语义分割的准确性。 我们的级联解码器可以嵌入现有网络, 并完全以端到端的方式进行培训。 级联解码器结构的目标是更有效地解码分层特性, 并且比已知的解码器更符合普通的编码器。 我们用我们的级联解码器替换了最新模型的解码器, 用于几项具有挑战性的生物医学图像分割任务, 所取得的重大改进显示了我们新的解码方法的功效。