Pyramid Medical Transformer for Medical Image Segmentation

Understand

Deep neural networks have been a prevailing technique in the field of medical image processing.

• However, the most popular convolutional neural networks (CNNs) based methods for medical image segmentation are imperfect because they model long-range dependencies by stacking layers or enlarging filters.
• Transformers and the self-attention mechanism are recently proposed to effectively learn long-range dependencies by modeling all pairs of word-to-word attention regardless of their positions.
• The idea has also been extended to the computer vision field by creating and treating image patches as embeddings.