139 citations · 765 across the 31 of their papers we have counts for
6 papers · 1 filter
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…
Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking Datasets
Yifan Peng, Shankai Yan, Zhiyong Lu
Inspired by the success of the General Language Understanding Evaluation benchmark, we introduce the Biomedical Language Understanding Evaluation (BLUE) benchmark to facilitate res…
A deep learning approach for automated detection of geographic atrophy from color fundus photographs
Tiarnan D. Keenan, Shazia Dharssi, Yifan Peng +5
Purpose: To assess the utility of deep learning in the detection of geographic atrophy (GA) from color fundus photographs; secondary aim to explore potential utility in detecting c…
A self-attention based deep learning method for lesion attribute detection from CT reports
Yifan Peng, Ke Yan, Veit Sandfort +2
In radiology, radiologists not only detect lesions from the medical image, but also describe them with various attributes such as their type, location, size, shape, and intensity.…
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…