Chest pathology detection using deep learning with non-medical training

Bar, Yaniv; Diamant, Idit; Wolf, Lior; Lieberman, Sivan; Konen, Eli; Greenspan, Hayit

doi:10.1109/isbi.2015.7163871

Cited by 308 publications

(180 citation statements)

References 12 publications

(19 reference statements)

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“…Bar et al 25 and Anavi et al 26 demonstrated the effectiveness of CNN-extracted features in characterizing chest x-rays, but did not provide comparison with traditional CADx methods as we report here. More specifically, Bar et al compared CNN-extracted features with general image descriptors such as GIST instead of CADx-specific features.…”

Section: Discussionmentioning

confidence: 69%

Digital mammographic tumor classification using transfer learning from deep convolutional neural networks

2016

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Abstract. Convolutional neural networks (CNNs) show potential for computer-aided diagnosis (CADx) by learning features directly from the image data instead of using analytically extracted features. However, CNNs are difficult to train from scratch for medical images due to small sample sizes and variations in tumor presentations. Instead, transfer learning can be used to extract tumor information from medical images via CNNs originally pretrained for nonmedical tasks, alleviating the need for large datasets. Our database includes 219 breast lesions (607 full-field digital mammographic images). We compared support vector machine classifiers based on the CNN-extracted image features and our prior computer-extracted tumor features in the task of distinguishing between benign and malignant breast lesions. Five-fold cross validation (by lesion) was conducted with the area under the receiver operating characteristic (ROC) curve as the performance metric. Results show that classifiers based on CNN-extracted features (with transfer learning) perform comparably to those using analytically extracted features [area under the ROC curve ðAUCÞ ¼ 0.81]. Further, the performance of ensemble classifiers based on both types was significantly better than that of either classifier type alone (AUC ¼ 0.86 versus 0.81, p ¼ 0.022). We conclude that transfer learning can improve current CADx methods while also providing standalone classifiers without large datasets, facilitating machine-learning methods in radiomics and precision medicine.

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Section: Discussionmentioning

confidence: 69%

Digital mammographic tumor classification using transfer learning from deep convolutional neural networks

2016

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“…Kumar et al [8] proposed a CAD system which uses deep features extracted from an autoencoder to classify lung nodules as either malignant or benign on LIDC database. In [9], Yaniv et al presented a system for medical application of chest pathology detection in x-rays which uses convolutional neural networks that are learned from a non-medical archive. that work showed a combination of deep learning (Decaf) and PiCodes features achieves the best performance.…”

Section: Methodsmentioning

confidence: 99%

“…This means that our classifier will not be able to correctly classify images in which cancerous nodules are located at the edge of the lung. To filter noise and include voxels from the edges, we use Marker-driven watershed segmentation, as described in Al-Tarawneh et al [9]. An original 2D CT slice of a sample patient is given in Figure 7.…”

Section: Watershedmentioning

confidence: 99%

Lung Cancer Detection and Classification with 3D Convolutional Neural Network (3D-CNN)

Alakwaa¹,

Nassef²,

Badr³

2017

ijacsa

184

100

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Abstract-This paper demonstrates a computer-aided diagnosis (CAD) system for lung cancer classification of CT scans with unmarked nodules, a dataset from the Kaggle Data Science Bowl, 2017. Thresholding was used as an initial segmentation approach to segment out lung tissue from the rest of the CT scan. Thresholding produced the next best lung segmentation. The initial approach was to directly feed the segmented CT scans into 3D CNNs for classification, but this proved to be inadequate. Instead, a modified U-Net trained on LUNA16 data (CT scans with labeled nodules) was used to first detect nodule candidates in the Kaggle CT scans. The U-Net nodule detection produced many false positives, so regions of CTs with segmented lungs where the most likely nodule candidates were located as determined by the U-Net output were fed into 3D Convolutional Neural Networks (CNNs) to ultimately classify the CT scan as positive or negative for lung cancer. The 3D CNNs produced a test set Accuracy of 86.6%. The performance of our CAD system outperforms the current CAD systems in literature which have several training and testing phases that each requires a lot of labeled data, while our CAD system has only three major phases (segmentation, nodule candidate detection, and malignancy classification), allowing more efficient training and detection and more generalizability to other cancers.

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“…The network was originally trained on natural images form the ImageNet competition and the network weight are available through caffe model zoo. Several medical imaging application has either used these trained weights as a feature extractor or to initialise there network [2]. This practice is called transfer learning and it helps overcome the problem of limited training data.…”

Section: Network Architecturementioning

confidence: 99%

Image Quality Classification for DR Screening Using Convolutional Neural Networks

Tennakoon

Mahapatra

Roy

et al. 2016

Proceedings of the Ophthalmic Medical Image Analysis Third International Workshop

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Abstract. The quality of input images significantly affects the outcome of automated diabetic retinopathy screening systems. Current methods to identify image quality rely on hand-crafted geometric and structural features, that does not generalize well. We propose a new method for retinal image quality classification (IQC) that uses computational algorithms imitating the working of the human visual systems. The proposed method leverages on learned supervised information using convolutional neural networks (CNN), thus avoiding hand-engineered features. Our analysis shows that the learned features capture both geometric and structural information relevant for image quality classification. Experimental results conducted on a relatively large dataset demonstrates that the overall method can achieve high accuracy. We also show that effective features for IQC can be learned by full training of shallow CNN as well as by using transfer learning.

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Chest pathology detection using deep learning with non-medical training

Cited by 308 publications

References 12 publications

Digital mammographic tumor classification using transfer learning from deep convolutional neural networks

Digital mammographic tumor classification using transfer learning from deep convolutional neural networks

Lung Cancer Detection and Classification with 3D Convolutional Neural Network (3D-CNN)

Image Quality Classification for DR Screening Using Convolutional Neural Networks

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