Re-thinking and Re-labeling LIDC-IDRI for Robust Pulmonary Cancer Prediction

no code implementations • 28 Jul 2022 • Hanxiao Zhang, Xiao Gu, Minghui Zhang, Weihao Yu, Liang Chen, Zhexin Wang, Feng Yao, Yun Gu, Guang-Zhong Yang

The LIDC-IDRI database is the most popular benchmark for lung cancer prediction.

Metric Learning Retrieval

Faithful learning with sure data for lung nodule diagnosis

no code implementations • 25 Feb 2022 • Hanxiao Zhang, Liang Chen, Xiao Gu, Minghui Zhang, Yulei Qin, Feng Yao, Zhexin Wang, Yun Gu, Guang-Zhong Yang

In this study, we construct a sure dataset with pathologically-confirmed labels and propose a collaborative learning framework to facilitate sure nodule classification by integrating unsure data knowledge through nodule segmentation and malignancy score regression.

Classification Lung Nodule Classification +1

Relationship between pulmonary nodule malignancy and surrounding pleurae, airways and vessels: a quantitative study using the public LIDC-IDRI dataset

no code implementations • 24 Jun 2021 • Yulei Qin, Yun Gu, Hanxiao Zhang, Jie Yang, Lihui Wang, Zhexin Wang, Feng Yao, Yue-Min Zhu

The correlation between nodules and the counting number of airways and vessels that contact or project towards nodules are respectively (OR=22. 96, \chi^2=105. 04) and (OR=7. 06, \chi^2=290. 11).

Computed Tomography (CT)