论文标题
通过注意力和多视图功能融合策略改善肾结石识别
Improved Kidney Stone Recognition Through Attention and Multi-View Feature Fusion Strategies
论文作者
论文摘要
这项贡献提出了一种深度学习方法，用于提取和融合与内窥镜不同观点获得的肾结石碎片有关的信息。在分类器训练期间，共同使用了表面和截面片段图像，以通过在每个卷积块的末端添加注意力层来提高特征的歧视能力。这种方法是专门设计的，以模仿生物学家在外活体中进行的形态宪法分析，以通过检查这两种观点来视觉上识别肾结石。在骨架上增加注意力机制的添加使单视图提取骨架的结果平均提高了4％。此外，与最先进的融合相比，深层特征的融合提高了总体结果，就肾结石分类精度而言，最高可提高11％。
This contribution presents a deep learning method for the extraction and fusion of information relating to kidney stone fragments acquired from different viewpoints of the endoscope. Surface and section fragment images are jointly used during the training of the classifier to improve the discrimination power of the features by adding attention layers at the end of each convolutional block. This approach is specifically designed to mimic the morpho-constitutional analysis performed in ex-vivo by biologists to visually identify kidney stones by inspecting both views. The addition of attention mechanisms to the backbone improved the results of single view extraction backbones by 4% on average. Moreover, in comparison to the state-of-the-art, the fusion of the deep features improved the overall results up to 11% in terms of kidney stone classification accuracy.
