Deep learning with non-medical training used for chest pathology identification

Abstract

In this work, we examine the strength of deep learning approaches for pathology detection in chest radiograph data. Convolutional neural networks (CNN) deep architecture classification approaches have gained popularity due to their ability to learn mid and high level image representations. We explore the ability of a CNN to identify different types of pathologies in chest x-ray images. Moreover, since very large training sets are generally not available in the medical domain, we explore the feasibility of using a deep learning approach based on non-medical learning. We tested our algorithm on a dataset of 93 images. We use a CNN that was trained with ImageNet, a well-known large scale nonmedical image database. The best performance was achieved using a combination of features extracted from the CNN and a set of low-level features. We obtained an area under curve (AUC) of 0.93 for Right Pleural Effusion detection, 0.89 for Enlarged heart detection and 0.79 for classification between healthy and abnormal chest x-ray, where all pathologies are combined into one large class. This is a first-of-its-kind experiment that shows that deep learning with large scale non-medical image databases may be sufficient for general medical image recognition tasks.

A survey on deep learning in medical image analysis

“…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)…”

Introduction to MAchine Learning & Knowledge Extraction (MAKE)

“…Important here is to emphasize that the features are not hand-crafted, instead fully automatically learned from the data, layer by layer, using a general-purpose learning procedure [4]. The practical value has been proven in different applications, e.g., in computer vision [47], natural language understanding [48], connectomics (study of brain circuits) [49], bioinformatics [50], health informatics [51][52][53], or in physics [54], to point only to a few examples. DL also contributes to advances in implementing human-level intelligence [55,56], hence contributes to cognitive science.…”

Guest Editorial Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique

“…The extracted outputs are considered features and are used to train a separate pattern classifier. For instance, in Bar et al [25], [26] pre-trained CNNs were used as a feature generator for chest pathology identification. In Ginneken et al [27] integration of CNN-based features with handcrafted features enabled improved performance in a nodule detection system.…”