False positive reduction in lymph node detection by using convolutional neural network with multi-view input

2020 ◽  
Author(s):  
Jiaqi Wang ◽  
Li Xu
Keyword(s):  
Neural Network ◽  
Lymph Node ◽  
Convolutional Neural Network ◽  
False Positive ◽  
False Positive Reduction

Multi-view Convolutional Neural Network for lung nodule false positive reduction

2020 ◽  
Vol 162 ◽  
pp. 113017 ◽  
Author(s):  
Salsabil Amin El-Regaily ◽  
Mohammed Abdel Megeed Salem ◽  
Mohamed Hassan Abdel Aziz ◽  
Mohamed Ismail Roushdy
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
False Positive ◽  
Lung Nodule ◽  
False Positive Reduction

Convolutional neural network-based PSO for lung nodule false positive reduction on CT images

2018 ◽  
Vol 162 ◽  
pp. 109-118 ◽  
Author(s):  
Giovanni Lucca França da Silva ◽  
Thales Levi Azevedo Valente ◽  
Aristófanes Corrêa Silva ◽  
Anselmo Cardoso de Paiva ◽  
Marcelo Gattass
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
False Positive ◽  
Lung Nodule ◽  
Ct Images ◽  
False Positive Reduction

scholarly journals Detection of Lung Nodules on CT Images based on the Convolutional Neural Network with Attention Mechanism

2021 ◽  
Vol 5 (2) ◽  
pp. 78-89
Author(s):  
Khai Dinh Lai ◽  
Thuy Thanh Nguyen ◽  
Thai Hoang Le
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
False Positive ◽  
Lung Nodule ◽  
Input Image ◽  
Attention Mechanism ◽  
Feature Maps ◽  
False Positive Reduction ◽  
Nodule Detection ◽  
Triplet Loss

The development of Computer-aided diagnosis (CAD) systems for automatic lung nodule detection through thoracic computed tomography (CT) scans has been an active area of research in recent years. Lung Nodule Analysis 2016 (LUNA16 challenge) encourages researchers to suggest a variety of successful nodule detection algorithms based on two key stages (1) candidates detection, (2) false-positive reduction. In the scope of this paper, a new convolutional neural network (CNN) architecture is proposed to efficiently solve the second challenge of LUNA16. Specifically, we find that typical CNN models pay little attention to the characteristics of input data, in order to address this constraint, we apply the attention-mechanism: propose a technique to attach Squeeze and Excitation-Block (SE-Block) after each convolution layer of CNN to emphasize important feature maps related to the characteristics of the input image - forming Attention sub-Convnet. The new CNN architecture is suggested by connecting the Attention sub-Convnets. In addition, we also analyze the selection of triplet loss or softmax loss functions to boost the rating performance of the proposed CNN. From the study, this is agreed to select softmax loss during the CNN training phase and triplet loss for the testing phase. Our suggested CNN is used to minimize the number of redundant candidates in order to improve the efficiency of false-positive reduction with the LUNA database. The results obtained in comparison to the previous models indicate the feasibility of the proposed model.

Multi-context 3D Resnet for Small-size False Positive Reduction in Pelvic Lymph Node Detection

2021 ◽  
Author(s):  
Zhen Pan ◽  
Shuo Huang ◽  
Han Wang ◽  
Mingtian Wei ◽  
Junjie Cui ◽  
...  
Keyword(s):  
Lymph Node ◽  
False Positive ◽  
Pelvic Lymph Node ◽  
False Positive Reduction
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