Multi-crop Convolutional Neural Networks for lung nodule malignancy suspiciousness classification

Shen, Wei; Zhou, Mu; Yang, Feng; Yu, Dongdong; Dong, Di; Yang, Caiyun; Zang, Yali; Tian, Jie

doi:10.1016/j.patcog.2016.05.029

Cited by 527 publications

(388 citation statements)

References 46 publications

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“…Emerging evidence suggests that sparse learning is useful for identifying multi-parametric prognostic imaging biomarkers in non-small cell lung cancer 72 . Second, recent breakthroughs in deep-learning with applications in radiology, such as lung nodule malignancy classification 59, 60 and lymph node detection 73 , have revealed encouraging performance in finding disease-specific imaging biomarkers. However, the availability of labeled medical data poses a significant challenge for developing efficient deep learning models.…”

Section: Research Opportunities and Challengesmentioning

confidence: 99%

Radiomics in Brain Tumor: Image Assessment, Quantitative Feature Descriptors, and Machine-Learning Approaches

Zhou¹,

Scott

Chaudhury

et al. 2017

AJNR Am J Neuroradiol

310

213

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Radiomics describes a broad set of computational methods that extract quantitative features from radiographic images. The resulting features can be used to inform imaging diagnosis, prognosis, and therapy response in oncology. However, major challenges remain for methodological developments to optimize feature extraction and provide rapid information flow in clinical settings. Equally important, to be clinically useful, predictive radiomic properties must be clearly linked to meaningful biological characteristics as well as qualitative imaging properties familiar to radiologist. Here we use a cross-disciplinary approach to highlight studies in radiomics. We review brain tumor radiological studies (e.g., imaging interpretation) through computational models (e.g., computer vision and machine learning) that provide novel clinical insights. In particular, we outline current quantitative image feature extraction and prediction strategies with different level of available clinical classes for supporting clinical decision making. We further discuss machine learning challenges and data opportunities to advance radiomic studies.

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Section: Research Opportunities and Challengesmentioning

confidence: 99%

Radiomics in Brain Tumor: Image Assessment, Quantitative Feature Descriptors, and Machine-Learning Approaches

Zhou¹,

Scott

Chaudhury

et al. 2017

AJNR Am J Neuroradiol

310

213

View full text Add to dashboard Cite

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“…Shen et al (18) investigated the likelihood of nodule malignancy in CT images by multi-crop CNN in which a novel multi-crop pooling strategy was proposed to crop different regions from convolutional feature maps and apply max-pooling different times. Experimental results showed that the proposed method achieves state-of-the-art nodule classification performance.…”

Section: Two-dimensional Convolutional Neural Networkmentioning

confidence: 99%

Deep learning aided decision support for pulmonary nodules diagnosing: a review

Yang¹,

Chi²,

Li³

et al. 2018

J. Thorac. Dis.

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Deep learning techniques have recently emerged as promising decision supporting approachesto automatically analyze medical images for different clinical diagnosing purposes. Diagnosing of pulmonary nodules by using computer-assisted diagnosing has received considerable theoretical, computational, and empirical research work, and considerable methods have been developed for detection and classification of pulmonary nodules on different formats of images including chest radiographs, computed tomography (CT), and positron emission tomography in the past five decades. The recent remarkable and significant progress in deep learning for pulmonary nodules achieved in both academia and the industry has demonstrated that deep learning techniques seem to be promising alternative decision support schemes to effectively tackle the central issues in pulmonary nodules diagnosing, including feature extraction, nodule detection, false-positive reduction, and benign-malignant classification for the huge volume of chest scan data. The main goal of this investigation is to provide a comprehensive state-of-the-art review of the deep learning aided decision support for pulmonary nodules diagnosing. As far as the authors know, this is the first time that a review is devoted exclusively to deep learning techniques for pulmonary nodules diagnosing.

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“…Meanwhile, because it is a difficult, tedious, and time‐consuming task for the radiologist to diagnose pulmonary nodules based on CT scans, computer‐aided diagnosis (CADx) scheme has been proposed and developed as a “second reader” to assist radiologists in their decision‐making . In order to build a CADx scheme, researchers have proposed and investigated two different architectures in CADx scheme developments namely, conventional CADx scheme and deep learning‐based convolutional neural networks CADx scheme . Although a large number of studies have been conducted to develop CADx schemes of lung nodules, most of these CADx schemes were built and evaluated by using lung nodules with only suspicious assessment rating scores provided by the radiologists, such as using the Lung Image Database Consortium and Image Database Resource Initiative (LIDC/IDRI) dataset .…”

Section: Introductionmentioning

confidence: 99%

Fusion of quantitative imaging features and serum biomarkers to improve performance of computer‐aided diagnosis scheme for lung cancer: A preliminary study

et al. 2018

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Objectives To develop and test a new multifeature‐based computer‐aided diagnosis (CADx) scheme of lung cancer by fusing quantitative imaging (QI) features and serum biomarkers to improve CADx performance in classifying between malignant and benign pulmonary nodules. Methods First, a dataset involving 173 patients was retrospectively assembled, which includes computed tomography (CT) images and five serum biomarkers extracted from blood samples. Second, a CADx scheme using a four‐step–based semiautomatic segmentation method was applied to segment the targeted lung nodules, and compute 78 QI features from each segmented nodule from CT images. Third, two support vector machine (SVM) classifiers were built using QI features and serum biomarkers, respectively. SVM classifiers were trained and tested using the overall dataset with a Relief feature selection method, a synthetic minority oversampling technique and a leave‐one‐case‐out validation method. Finally, to further improve CADx performance, an information‐fusion method was used to combine the prediction scores generated by two SVM classifiers. Results Areas under receiver operating characteristic curves (AUC) generated by QI feature and serum biomarker‐based SVMs were 0.81 ± 0.03 and 0.69 ± 0.05, respectively. Using an optimal weighted fusion method to combine prediction scores generated by two SVMs, AUC value significantly increased to 0.85 ± 0.03 (P < 0.05). Conclusions This study demonstrates (a) higher CADx performance by using QI features than using the serum biomarkers and (b) feasibility of further improving CADx performance by fusion of QI features and serum biomarkers, which indicates that QI features and serum biomarkers contain the complementary classification information.

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Multi-crop Convolutional Neural Networks for lung nodule malignancy suspiciousness classification

Cited by 527 publications

References 46 publications

Radiomics in Brain Tumor: Image Assessment, Quantitative Feature Descriptors, and Machine-Learning Approaches

Radiomics in Brain Tumor: Image Assessment, Quantitative Feature Descriptors, and Machine-Learning Approaches

Deep learning aided decision support for pulmonary nodules diagnosing: a review

Fusion of quantitative imaging features and serum biomarkers to improve performance of computer‐aided diagnosis scheme for lung cancer: A preliminary study

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