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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…
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…
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-…
A New 2.5D Representation for Lymph Node Detection using Random Sets of Deep Convolutional Neural Network Observations
Holger R. Roth, Le Lu, Ari Seff +6
Automated Lymph Node (LN) detection is an important clinical diagnostic task but very challenging due to the low contrast of surrounding structures in Computed Tomography (CT) and…
Coarse-to-Fine Classification via Parametric and Nonparametric Models for Computer-Aided Diagnosis
Meizhu Liu, Le Lu, Xiaojing Ye +1
Classification is one of the core problems in Computer-Aided Diagnosis (CAD), targeting for early cancer detection using 3D medical imaging interpretation. High detection sensitivi…