activity
20192021
most citedRelational Learning between Multiple Pulmonary Nodules via Deep Set Attention Transformers

26 citations · 71 across the 4 of their papers we have counts for

collaborators

13 papers

eess.IV202125 cited

RibSeg Dataset and Strong Point Cloud Baselines for Rib Segmentation from CT Scans

Jiancheng Yang, Shixuan Gu, Donglai Wei +2

Manual rib inspections in computed tomography (CT) scans are clinically critical but labor-intensive, as 24 ribs are typically elongated and oblique in 3D volumes. Automatic rib se…

cs.CV2021

3D Human Action Representation Learning via Cross-View Consistency Pursuit

Linguo Li, Minsi Wang, Bingbing Ni +3

In this work, we propose a Cross-view Contrastive Learning framework for unsupervised 3D skeleton-based action Representation (CrosSCLR), by leveraging multi-view complementary sup…

cs.CR2020

Learning Black-Box Attackers with Transferable Priors and Query Feedback

Jiancheng Yang, Yangzhou Jiang, Xiaoyang Huang +2

This paper addresses the challenging black-box adversarial attack problem, where only classification confidence of a victim model is available. Inspired by consistency of visual sa…

cs.LG2020

MIA-Prognosis: A Deep Learning Framework to Predict Therapy Response

Jiancheng Yang, Jiajun Chen, Kaiming Kuang +3

Predicting clinical outcome is remarkably important but challenging. Research efforts have been paid on seeking significant biomarkers associated with the therapy response or/and p…

eess.IV2020

Hierarchical Classification of Pulmonary Lesions: A Large-Scale Radio-Pathomics Study

Jiancheng Yang, Mingze Gao, Kaiming Kuang +4

Diagnosis of pulmonary lesions from computed tomography (CT) is important but challenging for clinical decision making in lung cancer related diseases. Deep learning has achieved g…

eess.IV2020

Learning Tumor Growth via Follow-Up Volume Prediction for Lung Nodules

Yamin Li, Jiancheng Yang, Yi Xu +5

Follow-up serves an important role in the management of pulmonary nodules for lung cancer. Imaging diagnostic guidelines with expert consensus have been made to help radiologists m…