Details

Autor(en) / Beteiligte

• Liu, Zhipeng
• Zhang, Zhiming
• Lei, Zhenvu
• Omura, Masaaki
• Wang, Rong-Long
• Gao, Shangce

Titel

Dendritic Deep Learning for Medical Segmentation

Ist Teil von

• IEEE/CAA journal of automatica sinica, 2024-03, Vol.11 (3), p.803-805

Ort / Verlag

Piscataway: Chinese Association of Automation (CAA)

Erscheinungsjahr

2024

Quelle

IEEE Xplore

Beschreibungen/Notizen

• Dear Editor, This letter presents a novel segmentation approach that leverages dendritic neurons to tackle the challenges of medical imaging segmentation. In this study, we enhance the segmentation accuracy based on a SegNet variant including an encoder-decoder structure, an upsampling index, and a deep supervision method. Furthermore, we introduce a dendritic neuron-based convolutional block to enable nonlinear feature mapping, thereby further improving the effectiveness of our approach. The proposed method is evaluated on medical imaging segmentation datasets, and the experimental results demonstrate that it is superior to state-of-the-art methods in terms of performance.