output
20082025
most citedThe Future of Digital Health with Federated Learning

3.1k citations

researchers with a paper here

Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.

collaborating institutions
Showing 2017 · cs.CVShow all

5 papers · 2 filters

cs.CV2017★ 15 cited

Deep Lesion Graphs in the Wild: Relationship Learning and Organization of Significant Radiology Image Findings in a Diverse Large-scale Lesion Database

Ke Yan, Xiaosong Wang, Le Lu +4

Radiologists in their daily work routinely find and annotate significant abnormalities on a large number of radiology images. Such abnormalities, or lesions, have collected over ye…

cs.CV2017★ 5 cited

Unsupervised Body Part Regression via Spatially Self-ordering Convolutional Neural Networks

Ke Yan, Le Lu, Ronald M. Summers

Automatic body part recognition for CT slices can benefit various medical image applications. Recent deep learning methods demonstrate promising performance, with the requirement o…

cs.CV2017

Feature Tracking Cardiac Magnetic Resonance via Deep Learning and Spline Optimization

Davis M. Vigneault, Weidi Xie, David A. Bluemke +1

Feature tracking Cardiac Magnetic Resonance (CMR) has recently emerged as an area of interest for quantification of regional cardiac function from balanced, steady state free prece…

cs.CV2017★ 14 cited

Spatial Aggregation of Holistically-Nested Convolutional Neural Networks for Automated Pancreas Localization and Segmentation

Holger R. Roth, Le Lu, Nathan Lay +4

Accurate and automatic organ segmentation from 3D radiological scans is an important yet challenging problem for medical image analysis. Specifically, the pancreas demonstrates ver…

cs.CV2017★ 7 cited

Unsupervised Joint Mining of Deep Features and Image Labels for Large-scale Radiology Image Categorization and Scene Recognition

Xiaosong Wang, Le Lu, Hoo-chang Shin +5

The recent rapid and tremendous success of deep convolutional neural networks (CNN) on many challenging computer vision tasks largely derives from the accessibility of the well-ann…