论文标题
多尺度上下文引导的腰椎疾病鉴定，并进行粗到细小的定位和分类
Multi-Scale Context-Guided Lumbar Spine Disease Identification with Coarse-to-fine Localization and Classification
论文作者
论文摘要
准确有效的腰椎疾病鉴定对于临床诊断至关重要。但是，现有的深度学习模型具有数百万个参数通常仅用数百或数十个医学图像就无法学习。这些模型还忽略了相邻对象之间的上下文关系，例如椎骨和椎间盘之间。这项工作介绍了一个多尺度的上下文引导的网络，其腰椎疾病鉴定具有粗到细节的定位和分类，称为CCF-NET。具体而言，在学习中，我们将本地化目标分为两个平行任务，即粗略和精细，它们更简单，有效地减少了参数和计算成本的数量。实验结果表明，以更少的参数和数据要求，粗到精细的设计具有实现高性能的潜力。此外，通过RESNET18和RESNET50，多尺度上下文指导的模块可以将性能显着提高6.45％和5.51％。我们的代码可在https://github.com/czifan/ccfnet.pytorch上找到。
Accurate and efficient lumbar spine disease identification is crucial for clinical diagnosis. However, existing deep learning models with millions of parameters often fail to learn with only hundreds or dozens of medical images. These models also ignore the contextual relationship between adjacent objects, such as between vertebras and intervertebral discs. This work introduces a multi-scale context-guided network with coarse-to-fine localization and classification, named CCF-Net, for lumbar spine disease identification. Specifically, in learning, we divide the localization objective into two parallel tasks, coarse and fine, which are more straightforward and effectively reduce the number of parameters and computational cost. The experimental results show that the coarse-to-fine design presents the potential to achieve high performance with fewer parameters and data requirements. Moreover, the multi-scale context-guided module can significantly improve the performance by 6.45% and 5.51% with ResNet18 and ResNet50, respectively. Our code is available at https://github.com/czifan/CCFNet.pytorch.
