43 citations · 83 across the 10 of their papers we have counts for
14 papers · 1 filter
Location-free Human Pose Estimation
Xixia Xu, Yingguo Gao, Ke Yan +2
Human pose estimation (HPE) usually requires large-scale training data to reach high performance. However, it is rather time-consuming to collect high-quality and fine-grained anno…
SIOD: Single Instance Annotated Per Category Per Image for Object Detection
Hanjun Li, Xingjia Pan, Ke Yan +2
Object detection under imperfect data receives great attention recently. Weakly supervised object detection (WSOD) suffers from severe localization issues due to the lack of instan…
Lesion Harvester: Iteratively Mining Unlabeled Lesions and Hard-Negative Examples at Scale
Jinzheng Cai, Adam P. Harrison, Youjing Zheng +5
Acquiring large-scale medical image data, necessary for training machine learning algorithms, is frequently intractable, due to prohibitive expert-driven annotation costs. Recent d…
Holistic and Comprehensive Annotation of Clinically Significant Findings on Diverse CT Images: Learning from Radiology Reports and Label Ontology
Ke Yan, Yifan Peng, Veit Sandfort +3
In radiologists' routine work, one major task is to read a medical image, e.g., a CT scan, find significant lesions, and describe them in the radiology report. In this paper, we st…
Fine-grained lesion annotation in CT images with knowledge mined from radiology reports
Ke Yan, Yifan Peng, Zhiyong Lu +1
In radiologists' routine work, one major task is to read a medical image, e.g., a CT scan, find significant lesions, and write sentences in the radiology report to describe them. I…
ULDor: A Universal Lesion Detector for CT Scans with Pseudo Masks and Hard Negative Example Mining
Youbao Tang, Ke Yan, Yuxing Tang +3
Automatic lesion detection from computed tomography (CT) scans is an important task in medical imaging analysis. It is still very challenging due to similar appearances (e.g. inten…