Multi-scale Convolutional Neural Networks for Lung Nodule Classification

A survey on deep learning in medical image analysis

“…Several authors have used multi-stream architectures to resolve this in a multi-scale fashion (Section 2.4.2). Shen et al (2015b) used three CNNs, each of which takes a nodule patch at a different scale as input. The resulting feature outputs of the three CNNs are then concatenated to form the final feature vector.…”

“…As summarized in Table 2, most works employ simple Anavi et al (2015) Image retrieval Combines classical features with those from pre-trained CNN for image retrieval using SVM Bar et al (2015) Pathology detection Features from a pre-trained CNN and low level features are used to detect various diseases Anavi et al (2016) Image retrieval Continuation of Anavi et al (2015), adding age and gender as features Bar et al (2016) Pathology detection Continuation of Bar et al (2015), more experiments and adding feature selection Cicero et al (2016) Pathology detection GoogLeNet CNN detects five common abnormalities, trained and validated on a large data set Tuberculosis detection Processes entire radiographs with a pre-trained fine-tuned network with 6 convolution layers Kim and Hwang (2016) Tuberculosis detection MIL framework produces heat map of suspicious regions via deconvolution Shin et al (2016a) Pathology detection CNN detects 17 diseases, large data set (7k images), recurrent networks produce short captions Rajkomar et al (2017) Frontal/lateral classification Pre-trained CNN performs frontal/lateral classification task Yang et al (2016c) Bone suppression Cascade of CNNs at increasing resolution learns bone images from gradients of radiographs Wang et al (2016a) Nodule classification Combines classical features with CNN features from pre-trained ImageNet CNN Used a standard feature extractor and a pre-trained CNN to classify detected lesions as benign peri-fissural nodules van Detects nodules with pre-trained CNN features from orthogonal patches around candidate, classified with SVM Shen et al (2015b) Three CNNs at different scales estimate nodule malignancy scores of radiologists (LIDC-IDRI data set) Chen et al (2016e) Combines features from CNN, SDAE and classical features to characterize nodules from LIDC-IDRI data set Ciompi et al (2016) Multi-stream CNN to classify nodules into subtypes: solid, part-solid, non-solid, calcified, spiculated, perifissural Dou et al (2016b) Uses 3D CNN around nodule candidates; ranks #1 in LUNA16 nodule detection challenge Li et al (2016a) Detects nodules with 2D CNN that processes small patches around a nodule Setio et al (2016) Detects nodules with end-to-end trained multi-stream CNN with 9 patches per candidate Shen et al (2016) 3D CNN classifies volume centered on nodule as benign/malignant, results are combined to patient level prediction Sun et al (2016b) Same dataset as Shen et al (2015b)…”

Self-paced Convolutional Neural Network for Computer Aided Detection in Medical Imaging Analysis

“…From an application perspective, there are various literature reports that applied CNN models to analyze medical images with lung diseases [15,16,17,18] and obtained encouraging results. As we discussed previously, the lack of training data is especially challenging in medical image analysis.…”

Medical Image Segmentation and Classification Using MKFCM and Hybrid Classifiers

“…Once the classification into Cavitary TB or Miliary TB has been carried out, it has also been proposed to classify the stages of TB accordingly as, Stage 1 or Stage 2 or Stage 3 based on the severity of infection by using ANN classifier. This hybrid classifier which is a combination of SVM classifier and ANN classifier is used to detect the TB in a short time with more accuracy, which perform well than the other existing methods [7][8][9][10]. The reduction of time required and complexities have been greatly reduced.…”