Asymmetric Adaptive Heterogeneous Network for Multi-Modality Medical Image Segmentation

Existing studies of multi-modality medical image segmentation tend to aggregate all modalities without discrimination and employ multiple symmetric encoders or decoders for feature extraction and fusion. They often overlook the different contributions to visual representation and intelligent decisions among multi-modality images. Motivated by this discovery, this paper proposes an asymmetric adaptive heterogeneous network for multi-modality image feature extraction with modality discrimination and adaptive fusion. For feature extraction, it uses a heterogeneous two-stream asymmetric feature-bridging network to extract complementary features from auxiliary multi-modality and leading single-modality images,…

• NF
  National Foundation for Science and Technology Development

  Award: KJZD-K202200606
• NN
  National Natural Science Foundation of China

  Awards: 61901074, 62221005, 62476033, 62206036, 62331008, 62236002, 61902046
• CP
  China Postdoctoral Science Foundation

  Award: 2021M693771
• NS
  Natural Science Foundation of Chongqing

  Awards: 2023NSCQ-LZX0045, CSTB2023NSCQ-MSX1065