Computer-aided mass detection in mammography: False positive reduction via gray-scale invariant ranklet texture features

2009 ◽  
Vol 36 (2) ◽  
pp. 311-316 ◽  
Author(s):  
Matteo Masotti ◽  
Nico Lanconelli ◽  
Renato Campanini
Keyword(s):  
False Positive ◽  
Texture Features ◽  
Mass Detection ◽  
Gray Scale ◽  
Scale Invariant ◽  
False Positive Reduction ◽  
Computer Aided

False-positive reduction in computer-aided mass detection using mammographic texture analysis and classification

2018 ◽  
Vol 160 ◽  
pp. 75-83 ◽  
Author(s):  
Sami Dhahbi ◽  
Walid Barhoumi ◽  
Jaroslaw Kurek ◽  
Bartosz Swiderski ◽  
Michal Kruk ◽  
...  
Keyword(s):  
Texture Analysis ◽  
False Positive ◽  
Mass Detection ◽  
False Positive Reduction ◽  
Computer Aided

scholarly journals Bilateral analysis based false positive reduction for computer-aided mass detection

2007 ◽  
Vol 34 (8) ◽  
pp. 3334-3344 ◽  
Author(s):  
Yi-Ta Wu ◽  
Jun Wei ◽  
Lubomir M. Hadjiiski ◽  
Berkman Sahiner ◽  
Chuan Zhou ◽  
...  
Keyword(s):  
False Positive ◽  
Mass Detection ◽  
False Positive Reduction ◽  
Computer Aided

A bilateral analysis scheme for false positive reduction in mammogram mass detection

2015 ◽  
Vol 57 ◽  
pp. 84-95 ◽  
Author(s):  
Yanfeng Li ◽  
Houjin Chen ◽  
Yongyi Yang ◽  
Lin Cheng ◽  
Lin Cao
Keyword(s):  
False Positive ◽  
Mass Detection ◽  
False Positive Reduction ◽  
Analysis Scheme

False-positive reduction using Hessian features in computer-aided detection of pulmonary nodules on thoracic CT images

2005 ◽  
Author(s):  
Berkman Sahiner ◽  
Zhanyu Ge ◽  
Heang-Ping Chan ◽  
Lubomir M. Hadjiiski ◽  
Naama Bogot ◽  
...  
Keyword(s):  
False Positive ◽  
Pulmonary Nodules ◽  
Ct Images ◽  
Computer Aided Detection ◽  
Thoracic Ct ◽  
False Positive Reduction ◽  
Computer Aided

Recognition of lesion correspondence on two mammographic views: a new method of false-positive reduction for computerized mass detection

2001 ◽  
Author(s):  
Berkman Sahiner ◽  
Nicholas Petrick ◽  
Heang-Ping Chan ◽  
Sophie Paquerault ◽  
Mark A. Helvie ◽  
...  
Keyword(s):  
False Positive ◽  
New Method ◽  
Mass Detection ◽  
False Positive Reduction

scholarly journals Multi-Scale Heterogeneous 3D CNN for False-Positive Reduction in Pulmonary Nodule Detection, Based on Chest CT Images

2019 ◽  
Vol 9 (16) ◽  
pp. 3261 ◽  
Author(s):  
Zhitao Xiao ◽  
Naichao Du ◽  
Lei Geng ◽  
Fang Zhang ◽  
Jun Wu ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Pulmonary Nodule ◽  
False Positive ◽  
Multi Scale ◽  
False Positive Reduction ◽  
Nodule Detection ◽  
Computer Aided ◽  
Pulmonary Nodule Detection ◽  
3D Cnn ◽  
Aided Diagnosis

Currently, lung cancer has one of the highest mortality rates because it is often caught too late. Therefore, early detection is essential to reduce the risk of death. Pulmonary nodules are considered key indicators of primary lung cancer. Developing an efficient and accurate computer-aided diagnosis system for pulmonary nodule detection is an important goal. Typically, a computer-aided diagnosis system for pulmonary nodule detection consists of two parts: candidate nodule extraction and false-positive reduction of candidate nodules. The reduction of false positives (FPs) of candidate nodules remains an important challenge due to morphological characteristics of nodule height changes and similar characteristics to other organs. In this study, we propose a novel multi-scale heterogeneous three-dimensional (3D) convolutional neural network (MSH-CNN) based on chest computed tomography (CT) images. There are three main strategies of the design: (1) using multi-scale 3D nodule blocks with different levels of contextual information as inputs; (2) using two different branches of 3D CNN to extract the expression features; (3) using a set of weights which are determined by back propagation to fuse the expression features produced by step 2. In order to test the performance of the algorithm, we trained and tested on the Lung Nodule Analysis 2016 (LUNA16) dataset, achieving an average competitive performance metric (CPM) score of 0.874 and a sensitivity of 91.7% at two FPs/scan. Moreover, our framework is universal and can be easily extended to other candidate false-positive reduction tasks in 3D object detection, as well as 3D object classification.

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