论文标题
少更多：甲状腺结节诊断的自适应课程学习
Less is More: Adaptive Curriculum Learning for Thyroid Nodule Diagnosis
论文作者
论文摘要
甲状腺结节分类旨在根据给定的超声图像确定结节是良性还是恶性。但是，通过细胞学活检获得的标签是临床医学的黄金标准，并不总是与超声成像Ti-Rads标准一致。两者之间的信息差异导致现有的基于深度学习的分类方法具有优柔寡断。为了解决不一致的标签问题，我们提出了一个自适应课程学习（ACL）框架，该框架可以自适应地发现并以不一致的标签丢弃样品。具体而言，ACL同时考虑了硬样品和模型确定性，并且可以准确确定用不一致的标签区分样品的阈值。此外，我们贡献了TNCD：甲状腺结节分类数据集，以促进对甲状腺结节的未来相关研究。基于三个不同的骨干网络的TNCD的广泛实验结果不仅证明了我们方法的优势，而且证明了较少的IS原理在战略上以不一致的标签抛弃样品可以产生性能提高。源代码和数据可从https://github.com/chenghui-666/acl/获得。
Thyroid nodule classification aims at determining whether the nodule is benign or malignant based on a given ultrasound image. However, the label obtained by the cytological biopsy which is the golden standard in clinical medicine is not always consistent with the ultrasound imaging TI-RADS criteria. The information difference between the two causes the existing deep learning-based classification methods to be indecisive. To solve the Inconsistent Label problem, we propose an Adaptive Curriculum Learning (ACL) framework, which adaptively discovers and discards the samples with inconsistent labels. Specifically, ACL takes both hard sample and model certainty into account, and could accurately determine the threshold to distinguish the samples with Inconsistent Label. Moreover, we contribute TNCD: a Thyroid Nodule Classification Dataset to facilitate future related research on the thyroid nodules. Extensive experimental results on TNCD based on three different backbone networks not only demonstrate the superiority of our method but also prove that the less-is-more principle which strategically discards the samples with Inconsistent Label could yield performance gains. Source code and data are available at https://github.com/chenghui-666/ACL/.
