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20142022
most citedHolistic Interstitial Lung Disease Detection using Deep Convolutional Neural Networks: Multi-label Learning and Unordered Pooling

10 citations · 22 across the 7 of their papers we have counts for

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5 papers · 1 filter

cs.CV20224 cited

Graph-Based Small Bowel Path Tracking with Cylindrical Constraints

Seung Yeon Shin, Sungwon Lee, Ronald M. Summers

We present a new graph-based method for small bowel path tracking based on cylindrical constraints. A distinctive characteristic of the small bowel compared to other organs is the…

cs.CV201710 cited

Holistic Interstitial Lung Disease Detection using Deep Convolutional Neural Networks: Multi-label Learning and Unordered Pooling

Mingchen Gao, Ziyue Xu, Le Lu +3

Accurately predicting and detecting interstitial lung disease (ILD) patterns given any computed tomography (CT) slice without any pre-processing prerequisites, such as manually del…

cs.CV20143 cited

2D View Aggregation for Lymph Node Detection Using a Shallow Hierarchy of Linear Classifiers

Ari Seff, Le Lu, Kevin M. Cherry +6

Enlarged lymph nodes (LNs) can provide important information for cancer diagnosis, staging, and measuring treatment reactions, making automated detection a highly sought goal. In t…

cs.CV20141 cited

A Bottom-Up Approach for Automatic Pancreas Segmentation in Abdominal CT Scans

Amal Farag, Le Lu, Evrim Turkbey +2

Organ segmentation is a prerequisite for a computer-aided diagnosis (CAD) system to detect pathologies and perform quantitative analysis. For anatomically high-variability abdomina…

cs.CV20141 cited

Detection of Sclerotic Spine Metastases via Random Aggregation of Deep Convolutional Neural Network Classifications

Holger R. Roth, Jianhua Yao, Le Lu +3

Automated detection of sclerotic metastases (bone lesions) in Computed Tomography (CT) images has potential to be an important tool in clinical practice and research. State-of-the-…