We propose a novel Computer-Aided Detection (CAD) system for pulmonary nodules using
multi-view convolutional networks (ConvNets), for which discriminative features are …

Automatic detection of pulmonary nodules in thoracic computed tomography (CT) scans has
been an active area of research for the last two decades. However, there have only been …

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS),
which was organized in conjunction with the IEEE International Symposium on …

The introduction of lung cancer screening programs will produce an unprecedented amount
of chest CT scans in the near future, which radiologists will have to read in order to decide …

Convolutional neural networks (CNNs) have emerged as the most powerful technique for a
range of different tasks in computer vision. Recent work suggested that CNN features are …

Subsolid pulmonary nodules occur less often than solid pulmonary nodules, but show a much
higher malignancy rate. Therefore, accurate detection of this type of pulmonary nodules is …

The morphology and structure of the submarine flanks of the Canary Islands were mapped
using the GLORIA long‐range side‐scan sonar system, bathymetric multibeam systems, and …

Purpose To examine the factors that affect inter- and intraobserver agreement for pulmonary
nodule type classification on low-radiation-dose computed tomographic (CT) images, and …

Background Accurate estimation of the malignancy risk of pulmonary nodules at chest CT is
crucial for optimizing management in lung cancer screening. Purpose To develop and …

Purpose: Current computer‐aided detection (CAD) systems for pulmonary nodules in computed
tomography (CT) scans have a good performance for relatively small nodules, but often …