RadFormer: Transformers with Global-Local Attention for Interpretable and Accurate Gallbladder Cancer Detection. (arXiv:2211.04793v1 [cs.CV])

We propose a novel deep neural network architecture to learn interpretable
representation for medical image analysis. Our architecture generates a global
attention for region of interest, and then learns bag of words style deep
feature embeddings with local attention. The global, and local feature maps are
combined using a contemporary transformer architecture for highly accurate
Gallbladder Cancer (GBC) detection from Ultrasound (USG) images. Our
experiments indicate that the detection accuracy of our model beats even human
radiologists, and advocates its …

arxiv attention cancer detection global local attention transformers