论文标题
Radformer：具有全球本地关注的变压器，可解释和准确的胆囊癌检测
RadFormer: Transformers with Global-Local Attention for Interpretable and Accurate Gallbladder Cancer Detection
论文作者
论文摘要
我们提出了一种新颖的深神经网络体系结构，以学习可解释的表示医学图像分析。我们的体系结构引起了人们对感兴趣地区的全球关注，然后学习了一袋单词样式的深层特征嵌入，并引起了本地关注。使用现代变压器结构将全球和局部特征地图合并，以从超声（USG）图像中进行高度准确的胆囊癌（GBC）检测。我们的实验表明，我们的模型的检测准确性甚至击败了人类放射科医生，并主张将其用作GBC诊断的第二读者。一袋嵌入单词可以探究我们的模型，以生成与医学文献中报道的GBC检测的可解释解释。我们表明，提出的模型不仅有助于了解神经网络模型的决策，还有助于发现与GBC诊断有关的新视觉特征。源代码和模型将在https://github.com/sbasu276/radformer上找到
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 use as the second reader for GBC diagnosis. Bag of words embeddings allow our model to be probed for generating interpretable explanations for GBC detection consistent with the ones reported in medical literature. We show that the proposed model not only helps understand decisions of neural network models but also aids in discovery of new visual features relevant to the diagnosis of GBC. Source-code and model will be available at https://github.com/sbasu276/RadFormer
