  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/.
