4 papers
SAM-Driven Weakly Supervised Nodule Segmentation with Uncertainty-Aware Cross Teaching
Xingyue Zhao, Peiqi Li, Xiangde Luo +3
Automated nodule segmentation is essential for computer-assisted diagnosis in ultrasound images. Nevertheless, most existing methods depend on precise pixel-level annotations by me…
Ultrasound Nodule Segmentation Using Asymmetric Learning with Simple Clinical Annotation
Xingyue Zhao, Zhongyu Li, Xiangde Luo +8
Recent advances in deep learning have greatly facilitated the automated segmentation of ultrasound images, which is essential for nodule morphological analysis. Nevertheless, most…
Asymmetric Co-Training with Explainable Cell Graph Ensembling for Histopathological Image Classification
Ziqi Yang, Zhongyu Li, Chen Liu +7
Convolutional neural networks excel in histopathological image classification, yet their pixel-level focus hampers explainability. Conversely, emerging graph convolutional networks…
Key-frame Guided Network for Thyroid Nodule Recognition using Ultrasound Videos
Yuchen Wang, Zhongyu Li, Xiangxiang Cui +4
Ultrasound examination is widely used in the clinical diagnosis of thyroid nodules (benign/malignant). However, the accuracy relies heavily on radiologist experience. Although deep…