718 citations · 1.8k across the 30 of their papers we have counts for
12 papers · 1 filter
-net: Systematic Evaluation of Iterative Deep Neural Networks for Fast Parallel MR Image Reconstruction
Kerstin Hammernik, Jo Schlemper, Chen Qin +3
Purpose: To systematically investigate the influence of various data consistency layers, (semi-)supervised learning and ensembling strategies, defined in a -net, for accelerated…
-net: Ensembled Iterative Deep Neural Networks for Accelerated Parallel MR Image Reconstruction
Jo Schlemper, Chen Qin, Jinming Duan +2
We explore an ensembled -net for fast parallel MR imaging, including parallel coil networks, which perform implicit coil weighting, and sensitivity networks, involving explicit…
TUNA-Net: Task-oriented UNsupervised Adversarial Network for Disease Recognition in Cross-Domain Chest X-rays
Yuxing Tang, Youbao Tang, Veit Sandfort +2
In this work, we exploit the unsupervised domain adaptation problem for radiology image interpretation across domains. Specifically, we study how to adapt the disease recognition m…
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…
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…