129 citations · 142 across the 3 of their papers we have counts for
7 papers
MULAN: Multitask Universal Lesion Analysis Network for Joint Lesion Detection, Tagging, and Segmentation
Ke Yan, Youbao Tang, Yifan Peng +4
When reading medical images such as a computed tomography (CT) scan, radiologists generally search across the image to find lesions, characterize and measure them, and then describ…
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…
Spatio-Temporal Convolutional LSTMs for Tumor Growth Prediction by Learning 4D Longitudinal Patient Data
Ling Zhang, Le Lu, Xiaosong Wang +4
Prognostic tumor growth modeling via volumetric medical imaging observations can potentially lead to better outcomes of tumor treatment and surgical planning. Recent advances of co…
3D Context Enhanced Region-based Convolutional Neural Network for End-to-End Lesion Detection
Ke Yan, Mohammadhadi Bagheri, Ronald M. Summers
Detecting lesions from computed tomography (CT) scans is an important but difficult problem because non-lesions and true lesions can appear similar. 3D context is known to be helpf…
Semi-Automatic RECIST Labeling on CT Scans with Cascaded Convolutional Neural Networks
Youbao Tang, Adam P. Harrison, Mohammadhadi Bagheri +2
Response evaluation criteria in solid tumors (RECIST) is the standard measurement for tumor extent to evaluate treatment responses in cancer patients. As such, RECIST annotations m…
Deep LOGISMOS: Deep Learning Graph-based 3D Segmentation of Pancreatic Tumors on CT scans
Zhihui Guo, Ling Zhang, Le Lu +4
This paper reports Deep LOGISMOS approach to 3D tumor segmentation by incorporating boundary information derived from deep contextual learning to LOGISMOS - layered optimal graph i…