Learning from Weakly-Labeled Clinical Data for Automatic Thyroid Nodule Classification in Ultrasound Images
Jian-Xiong Wang, Shuai Li, Wenfeng Song, Hong Ying Qin, Qi Zhang, Aimin Hao · 2018
This paper proposes a semi-supervised learning method based on weakly-labeled data to automatically classify ultrasound (US) thyroid nodules. Key to our new approach is the unification of multi-instance learning (MIL) with deep learning. Benefiting from that, our method can directly use off-the-shelf clinical data, which involves no labels to indicate nodule classes. To this end, we take the US images of a patient as a bag, and take the corresponding pathology report as the bag label. Specifically, we first propose a bag generating method, wherein the detected thyroid nodules are considered as instances corresponding to certain bag. After that, we design an effective EM algorithm to train a convolutional neural network (CNN) for nodule classification. We conduct extensive experiments and comprehensive evaluations on different datasets, and all the experiments confirm that, our method significantly outperforms state-of-the-art MIL algorithms, which exhibits great potential in clinical applications.