Lung Nodule Classification using Deep Local-Global Networks

Purpose: Lung nodules have very diverse shapes and sizes, which makes classifying them as benign/malignant a challenging problem. In this paper, we propose a novel method to predict the malignancy of nodules that have the capability to analyze the shape and size of a nodule using a global feature extractor, as well as the density and structure of the nodule using a local feature extractor... (read more)

PDF Abstract

Datasets


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK BENCHMARK
Lung Nodule Classification LIDC-IDRI Local-Global Accuracy 88.46 # 5
Accuracy(10-fold) 88.46 # 2
AUC 95.62 # 2

Methods used in the Paper


METHOD TYPE
Average Pooling
Pooling Operations
Non-Local Operation
Image Feature Extractors
Concatenated Skip Connection
Skip Connections
Dense Block
Image Model Blocks
Dropout
Regularization
Dense Connections
Feedforward Networks
Softmax
Output Functions
Non-Local Block
Image Model Blocks
DenseNet
Convolutional Neural Networks
ReLU
Activation Functions
1x1 Convolution
Convolutions
Batch Normalization
Normalization
Bottleneck Residual Block
Skip Connection Blocks
Global Average Pooling
Pooling Operations
Residual Block
Skip Connection Blocks
Kaiming Initialization
Initialization
Max Pooling
Pooling Operations
Residual Connection
Skip Connections
Convolution
Convolutions
ResNet
Convolutional Neural Networks