The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Marcelo Gattass - One of the best experts on this subject based on the ideXlab platform.
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convolutional neural network based pso for Lung Nodule false positive reduction on ct images
Computer Methods and Programs in Biomedicine, 2018Co-Authors: Giovanni Lucca Franca Da Silva, Aristofanes Correa Silva, Anselmo Cardoso De Paiva, Thales Levi Azevedo Valente, Marcelo GattassAbstract:Abstract Background and objective Detection of Lung Nodules is critical in CAD systems; this is because of their similar contrast with other structures and low density, which result in the generation of numerous false positives (FPs). Therefore, this study proposes a methodology to reduce the FP number using a deep learning technique in conjunction with an evolutionary technique. Method The particle swarm optimization (PSO) algorithm was used to optimize the network hyperparameters in the convolutional neural network (CNN) in order to enhance the network performance and eliminate the requirement of manual search. Results The methodology was tested on computed tomography (CT) scans from the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) with the highest accuracy of 97.62%, sensitivity of 92.20%, specificity of 98.64%, and area under the receiver operating characteristic (ROC) curve of 0.955. Conclusion The results demonstrate the high performance-potential of the PSO algorithm in the identification of optimal CNN hyperparameters for Lung Nodule candidate classification into Nodules and non-Nodules, increasing the sensitivity rates in the FP reduction step of CAD systems.
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Lung Nodule classification using artificial crawlers directional texture and support vector machine
Expert Systems With Applications, 2017Co-Authors: Bruno Rodrigues Froz, Antonio Oseas De Carvalho Filho, Aristofanes Correa Silva, Anselmo Cardoso De Paiva, Rodolfo Acatauassu Nunes, Marcelo GattassAbstract:Abstract Lung cancer is the major cause of death among patients with cancer throughout the world. The main symptom that indicate the Lung cancer is the presence of Lung Nodules. This work proposes a methodology to classify Lung Nodule and non-Nodule using texture features. The state-of-art of the presented work are the adaption of the Artificial Crawlers and Rose Diagram techniques for representing patterns over 3D images. Several information are extracted based on the texture behavior of these methods, allowing the correct classification of Lung Nodules candidates using Support Vector. Objective: This work proposes a methodology to classify Lung Nodule candidates and non-Nodule candidates based on computed tomography (CT) images. Methodology: The Lung Image Database Consortium (LIDC-IDRI) image database is employed for our tests. Three techniques are employed to extract texture measurements. The first technique is artificial crawlers (ACs), an artificial life algorithm. The second technique uses the rose diagram (RD) to extract directional measurements. The third technique is a hybrid model that combines texture measurements from artificial crawlers and the rose diagram. The support vector machine (SVM) classifier with a radial basis kernel is employed. Results: In the testing stage, we used 833 scans from the LIDC-IDRI database. For the application of the methodology, we decided to divide the whole database into two groups, training and testing. We used partitions of training and testing of 20/80%, 40/60%, 60/40% and 80/20%. The division was repeated 5 times at random. We reached a mean accuracy (mACC) of 94.30%, a mean sensitivity (mSEN) of 91.86%, a mean specificity (mSPC) of 94.78%, a coefficient of accuracy variance (CAv) of 1.61% and a mean area under the receiver operating characteristic (mROC) curves of 0.922. Conclusion: Lung cancer has the highest mortality rate and one of the smallest survival rates after diagnosis. An early diagnosis increases the survival chance of patients. The proposed methodology is a useful tool for specialists in the detection of Nodules. We believe we contribute for the expert system field because 1) the adaption of the Artificial Crawlers and Rose Diagram methods as 3D texture descriptors is innovative and contains great potential; 2) we adapted and developed measurements from the 3D texture descriptors; and 3) the simplicity and discriminative power of the methodology can be extended to applications based on images with other contexts.
Lei Zhang - One of the best experts on this subject based on the ideXlab platform.
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mscs deepln evaluating Lung Nodule malignancy using multi scale cost sensitive neural networks
Medical Image Analysis, 2020Co-Authors: Chengdi Wang, Jixiang Guo, Yuncui Gan, Jianyong Wang, Hongli Bai, Lei ZhangAbstract:Abstract The accurate identification of malignant Lung Nodules using computed tomography (CT) screening images is vital for the early detection of Lung cancer. It also offers patients the best chance of cure, because non-invasive CT imaging has the ability to capture intra-tumoral heterogeneity. Deep learning methods have obtained promising results for the malignancy identification problem; however, two substantial challenges still remain. First, small datasets cannot insufficiently train the model and tend to overfit it. Second, category imbalance in the data is a problem. In this paper, we propose a method called MSCS-DeepLN that evaluates Lung Nodule malignancy and simultaneously solves these two problems. Three light models are trained and combined to evaluate the malignancy of a Lung Nodule. Three-dimensional convolutional neural networks (CNNs) are employed as the backbone of each light model to extract the Lung Nodule features from CT images and preserve Lung Nodule spatial heterogeneity. Multi-scale input cropped from CT images enables the sub-networks to learn the multi-level contextual features and preserve diverse. To tackle the imbalance problem, our proposed method employs an AUC approximation as the penalty term. During training, the error in this penalty term is generated from each major and minor class pair, so that negatives and positives can contribute equally to updating this model. Based on these methods, we obtain state-of-the-art results on the LIDC-IDRI dataset. Furthermore, we constructed a new dataset collected from a grade-A tertiary hospital and annotated using biopsy-based cytological analysis to verify the performance of our method in clinical practice.
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accuracy of a 3 dimensionally printed navigational template for localizing small pulmonary Nodules a noninferiority randomized clinical trial
JAMA Surgery, 2019Co-Authors: Lei Zhang, Long Wang, Xiermaimaiti Kadeer, Li Zeyao, Xiwen Sun, Weiyan Sun, Yunlang She, Dong Xie, Liling ZouAbstract:Importance Localization of small Lung Nodules are challenging because of the difficulty of Nodule recognition during video-assisted thoracoscopic surgery. Using 3-dimensional (3-D) printing technology, a navigational template was recently created to assist percutaneous Lung Nodule localization; however, the efficacy and safety of this template have not yet been evaluated. Objective To assess the noninferiority of the efficacy and safety of a 3-D-printed navigational template guide for localizing small peripheral Lung Nodules. Design, Setting, and Participants This noninferiority randomized clinical trial conducted between October 2016 and October 2017 at Shanghai Pulmonary Hospital, Shanghai, China, compared the safety and precision of Lung Nodule localization using a template-guided approach vs the conventional computed tomography (CT)-guided approach. In total, 213 surgical candidates with small peripheral Lung Nodules (<2 cm) were recruited to undergo either CT- or template-guided Lung Nodule localization. An intention-to-treat analysis was conducted. Interventions Percutaneous Lung Nodule localization. Main Outcomes and Measures The primary outcome was the accuracy of Lung Nodule localization (localizer deviation), and secondary outcomes were procedural duration, radiation dosage, and complication rate. Results Of the 200 patients randomized at a ratio of 1:1 to the template- and CT-guided groups, most were women (147 vs 53), body mass index ranged from 15.4 to 37.3, the mean (SD) Nodule size was 9.7 (2.9) mm, and the mean distance between the outer edge of target Nodule and the pleura was 7.8 (range, 0.0-43.9) mm. In total, 190 patients underwent either CT- or template-guided Lung Nodule localization and subsequent surgery. Among these patients, localizer deviation did not significantly differ between the template- and CT-guided groups (mean [SD], 8.7 [6.9] vs 9.6 [5.8] mm; P = .36). The mean (SD) procedural durations were 7.4 (3.2) minutes for the template-guided group and 9.5 (3.6) minutes for the CT-guided group (P < .001). The mean (SD) radiation dose was 229 (65) mGy × cm in the template-guided group and 313 (84) mGy × cm in CT-guided group (P < .001). Conclusions and Relevance The use of the 3-D-printed navigational template for localization of small peripheral Lung Nodules showed efficacy and safety that were not substantially worse than those for the CT-guided approach while significantly simplifying the localization procedure and decreasing patient radiation exposure. Trial Registration ClinicalTrials.gov identifier: NCT02952261.
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accuracy of a 3 dimensionally printed navigational template for localizing small pulmonary Nodules a noninferiority randomized clinical trial
JAMA Surgery, 2019Co-Authors: Lei Zhang, Long Wang, Xiermaimaiti Kadeer, Li Zeyao, Mu Li, Gaetano Rocco, Ping Yang, Chang Chen, Rene Horsleben Petersen, Calvin S H NgAbstract:Importance Localization of small Lung Nodules are challenging because of the difficulty of Nodule recognition during video-assisted thoracoscopic surgery. Using 3-dimensional (3-D) printing technology, a navigational template was recently created to assist percutaneous Lung Nodule localization; however, the efficacy and safety of this template have not yet been evaluated. Objective To assess the noninferiority of the efficacy and safety of a 3-D–printed navigational template guide for localizing small peripheral Lung Nodules. Design, Setting, and Participants This noninferiority randomized clinical trial conducted between October 2016 and October 2017 at Shanghai Pulmonary Hospital, Shanghai, China, compared the safety and precision of Lung Nodule localization using a template-guided approach vs the conventional computed tomography (CT)-guided approach. In total, 213 surgical candidates with small peripheral Lung Nodules ( Interventions Percutaneous Lung Nodule localization. Main Outcomes and Measures The primary outcome was the accuracy of Lung Nodule localization (localizer deviation), and secondary outcomes were procedural duration, radiation dosage, and complication rate. Results Of the 200 patients randomized at a ratio of 1:1 to the template- and CT-guided groups, most were women (147 vs 53), body mass index ranged from 15.4 to 37.3, the mean (SD) Nodule size was 9.7 (2.9) mm, and the mean distance between the outer edge of target Nodule and the pleura was 7.8 (range, 0.0-43.9) mm. In total, 190 patients underwent either CT- or template-guided Lung Nodule localization and subsequent surgery. Among these patients, localizer deviation did not significantly differ between the template- and CT-guided groups (mean [SD], 8.7 [6.9] vs 9.6 [5.8] mm; P = .36). The mean (SD) procedural durations were 7.4 (3.2) minutes for the template-guided group and 9.5 (3.6) minutes for the CT-guided group ( P P Conclusions and Relevance The use of the 3-D–printed navigational template for localization of small peripheral Lung Nodules showed efficacy and safety that were not substantially worse than those for the CT-guided approach while significantly simplifying the localization procedure and decreasing patient radiation exposure. Trial Registration ClinicalTrials.gov identifier:NCT02952261
Jie Tian - One of the best experts on this subject based on the ideXlab platform.
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central focused convolutional neural networks developing a data driven model for Lung Nodule segmentation
Medical Image Analysis, 2017Co-Authors: Shuo Wang, Mu Zhou, Di Dong, Yali Zang, Olivier Gevaert, Zhenyu Liu, Zaiyi Liu, Jie TianAbstract:Accurate Lung Nodule segmentation from computed tomography (CT) images is of great importance for image-driven Lung cancer analysis. However, the heterogeneity of Lung Nodules and the presence of similar visual characteristics between Nodules and their surroundings make it difficult for robust Nodule segmentation. In this study, we propose a data-driven model, termed the Central Focused Convolutional Neural Networks (CF-CNN), to segment Lung Nodules from heterogeneous CT images. Our approach combines two key insights: 1) the proposed model captures a diverse set of Nodule-sensitive features from both 3-D and 2-D CT images simultaneously; 2) when classifying an image voxel, the effects of its neighbor voxels can vary according to their spatial locations. We describe this phenomenon by proposing a novel central pooling layer retaining much information on voxel patch center, followed by a multi-scale patch learning strategy. Moreover, we design a weighted sampling to facilitate the model training, where training samples are selected according to their degree of segmentation difficulty. The proposed method has been extensively evaluated on the public LIDC dataset including 893 Nodules and an independent dataset with 74 Nodules from Guangdong General Hospital (GDGH). We showed that CF-CNN achieved superior segmentation performance with average dice scores of 82.15% and 80.02% for the two datasets respectively. Moreover, we compared our results with the inter-radiologists consistency on LIDC dataset, showing a difference in average dice score of only 1.98%.
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a multi view deep convolutional neural networks for Lung Nodule segmentation
International Conference of the IEEE Engineering in Medicine and Biology Society, 2017Co-Authors: Shuo Wang, Mu Zhou, Di Dong, Olivier Gevaert, Zhenchao Tang, Zhenyu Liu, Jie TianAbstract:We present a multi-view convolutional neural networks (MV-CNN) for Lung Nodule segmentation. The MV-CNN specialized in capturing a diverse set of Nodule-sensitive features from axial, coronal and sagittal views in CT images simultaneously. The proposed network architecture consists of three CNN branches, where each branch includes seven stacked layers and takes multi-scale Nodule patches as input. The three CNN branches are then integrated with a fully connected layer to predict whether the patch center voxel belongs to the Nodule. The proposed method has been evaluated on 893 Nodules from the public LIDC-IDRI dataset, where ground-truth annotations and CT imaging data were provided. We showed that MV-CNN demonstrated encouraging performance for segmenting various type of Nodules including juxta-pleural, cavitary, and non-solid Nodules, achieving an average dice similarity coefficient (DSC) of 77.67% and average surface distance (ASD) of 0.24, outperforming conventional image segmentation approaches.
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multi crop convolutional neural networks for Lung Nodule malignancy suspiciousness classification
Pattern Recognition, 2017Co-Authors: Wei Shen, Mu Zhou, Feng Yang, Caiyun Yang, Di Dong, Yali Zang, Jie TianAbstract:We investigate the problem of Lung Nodule malignancy suspiciousness (the likelihood of Nodule malignancy) classification using thoracic Computed Tomography (CT) images. Unlike traditional studies primarily relying on cautious Nodule segmentation and time-consuming feature extraction, we tackle a more challenging task on directly modeling raw Nodule patches and building an end-to-end machine-learning architecture for classifying Lung Nodule malignancy suspiciousness. We present a Multi-crop Convolutional Neural Network (MC-CNN) to automatically extract Nodule salient information by employing a novel multi-crop pooling strategy which crops different regions from convolutional feature maps and then applies max-pooling different times. Extensive experimental results show that the proposed method not only achieves state-of-the-art Nodule suspiciousness classification performance, but also effectively characterizes Nodule semantic attributes (subtlety and margin) and Nodule diameter which are potentially helpful in modeling Nodule malignancy
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multi scale convolutional neural networks for Lung Nodule classification
International Conference Information Processing, 2015Co-Authors: Wei Shen, Mu Zhou, Feng Yang, Caiyun Yang, Jie TianAbstract:We investigate the problem of diagnostic Lung Nodule classification using thoracic Computed Tomography (CT) screening. Unlike traditional studies primarily relying on Nodule segmentation for regional analysis, we tackle a more challenging problem on directly modelling raw Nodule patches without any prior definition of Nodule morphology. We propose a hierarchical learning framework—Multi-scale Convolutional Neural Networks (MCNN)—to capture Nodule heterogeneity by extracting discriminative features from alternatingly stacked layers. In particular, to sufficiently quantify Nodule characteristics, our framework utilizes multi-scale Nodule patches to learn a set of class-specific features simultaneously by concatenating response neuron activations obtained at the last layer from each input scale. We evaluate the proposed method on CT images from Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI), where both Lung Nodule screening and Nodule annotations are provided. Experimental results demonstrate the effectiveness of our method on classifying malignant and benign Nodules without Nodule segmentation.
Liling Zou - One of the best experts on this subject based on the ideXlab platform.
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accuracy of a 3 dimensionally printed navigational template for localizing small pulmonary Nodules a noninferiority randomized clinical trial
JAMA Surgery, 2019Co-Authors: Lei Zhang, Long Wang, Xiermaimaiti Kadeer, Li Zeyao, Xiwen Sun, Weiyan Sun, Yunlang She, Dong Xie, Liling ZouAbstract:Importance Localization of small Lung Nodules are challenging because of the difficulty of Nodule recognition during video-assisted thoracoscopic surgery. Using 3-dimensional (3-D) printing technology, a navigational template was recently created to assist percutaneous Lung Nodule localization; however, the efficacy and safety of this template have not yet been evaluated. Objective To assess the noninferiority of the efficacy and safety of a 3-D-printed navigational template guide for localizing small peripheral Lung Nodules. Design, Setting, and Participants This noninferiority randomized clinical trial conducted between October 2016 and October 2017 at Shanghai Pulmonary Hospital, Shanghai, China, compared the safety and precision of Lung Nodule localization using a template-guided approach vs the conventional computed tomography (CT)-guided approach. In total, 213 surgical candidates with small peripheral Lung Nodules (<2 cm) were recruited to undergo either CT- or template-guided Lung Nodule localization. An intention-to-treat analysis was conducted. Interventions Percutaneous Lung Nodule localization. Main Outcomes and Measures The primary outcome was the accuracy of Lung Nodule localization (localizer deviation), and secondary outcomes were procedural duration, radiation dosage, and complication rate. Results Of the 200 patients randomized at a ratio of 1:1 to the template- and CT-guided groups, most were women (147 vs 53), body mass index ranged from 15.4 to 37.3, the mean (SD) Nodule size was 9.7 (2.9) mm, and the mean distance between the outer edge of target Nodule and the pleura was 7.8 (range, 0.0-43.9) mm. In total, 190 patients underwent either CT- or template-guided Lung Nodule localization and subsequent surgery. Among these patients, localizer deviation did not significantly differ between the template- and CT-guided groups (mean [SD], 8.7 [6.9] vs 9.6 [5.8] mm; P = .36). The mean (SD) procedural durations were 7.4 (3.2) minutes for the template-guided group and 9.5 (3.6) minutes for the CT-guided group (P < .001). The mean (SD) radiation dose was 229 (65) mGy × cm in the template-guided group and 313 (84) mGy × cm in CT-guided group (P < .001). Conclusions and Relevance The use of the 3-D-printed navigational template for localization of small peripheral Lung Nodules showed efficacy and safety that were not substantially worse than those for the CT-guided approach while significantly simplifying the localization procedure and decreasing patient radiation exposure. Trial Registration ClinicalTrials.gov identifier: NCT02952261.
Yong Xia - One of the best experts on this subject based on the ideXlab platform.
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semi supervised adversarial model for benign malignant Lung Nodule classification on chest ct
Medical Image Analysis, 2019Co-Authors: Yutong Xie, Jianpeng Zhang, Yong XiaAbstract:Classification of benign-malignant Lung Nodules on chest CT is the most critical step in the early detection of Lung cancer and prolongation of patient survival. Despite their success in image classification, deep convolutional neural networks (DCNNs) always require a large number of labeled training data, which are not available for most medical image analysis applications due to the work required in image acquisition and particularly image annotation. In this paper, we propose a semi-supervised adversarial classification (SSAC) model that can be trained by using both labeled and unlabeled data for benign-malignant Lung Nodule classification. This model consists of an adversarial autoencoder-based unsupervised reconstruction network R, a supervised classification network C, and learnable transition layers that enable the adaption of the image representation ability learned by R to C. The SSAC model has been extended to the multi-view knowledge-based collaborative learning, aiming to employ three SSACs to characterize each Nodule's overall appearance, heterogeneity in shape and texture, respectively, and to perform such characterization on nine planar views. The MK-SSAC model has been evaluated on the benchmark LIDC-IDRI dataset and achieves an accuracy of 92.53% and an AUC of 95.81%, which are superior to the performance of other Lung Nodule classification and semi-supervised learning approaches.
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knowledge based collaborative deep learning for benign malignant Lung Nodule classification on chest ct
IEEE Transactions on Medical Imaging, 2019Co-Authors: Yutong Xie, Yang Song, Dagan Feng, Michael J Fulham, Jianpeng Zhang, Yong Xia, Weidong CaiAbstract:The accurate identification of malignant Lung Nodules on chest CT is critical for the early detection of Lung cancer, which also offers patients the best chance of cure. Deep learning methods have recently been successfully introduced to computer vision problems, although substantial challenges remain in the detection of malignant Nodules due to the lack of large training data sets. In this paper, we propose a multi-view knowledge-based collaborative (MV-KBC) deep model to separate malignant from benign Nodules using limited chest CT data. Our model learns 3-D Lung Nodule characteristics by decomposing a 3-D Nodule into nine fixed views. For each view, we construct a knowledge-based collaborative (KBC) submodel, where three types of image patches are designed to fine-tune three pre-trained ResNet-50 networks that characterize the Nodules’ overall appearance, voxel, and shape heterogeneity, respectively. We jointly use the nine KBC submodels to classify Lung Nodules with an adaptive weighting scheme learned during the error back propagation, which enables the MV-KBC model to be trained in an end-to-end manner. The penalty loss function is used for better reduction of the false negative rate with a minimal effect on the overall performance of the MV-KBC model. We tested our method on the benchmark LIDC-IDRI data set and compared it to the five state-of-the-art classification approaches. Our results show that the MV-KBC model achieved an accuracy of 91.60% for Lung Nodule classification with an AUC of 95.70%. These results are markedly superior to the state-of-the-art approaches.
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transferable multi model ensemble for benign malignant Lung Nodule classification on chest ct
Medical Image Computing and Computer-Assisted Intervention, 2017Co-Authors: Yutong Xie, Michael J Fulham, Jianpeng Zhang, Yong Xia, David Dagan Feng, Weidong CaiAbstract:The classification of benign versus malignant Lung Nodules using chest CT plays a pivotal role in the early detection of Lung cancer and this early detection has the best chance of cure. Although deep learning is now the most successful solution for image classification problems, it requires a myriad number of training data, which are not usually readily available for most routine medical imaging applications. In this paper, we propose the transferable multi-model ensemble (TMME) algorithm to separate malignant from benign Lung Nodules using limited chest CT data. This algorithm transfers the image representation abilities of three ResNet-50 models, which were pre-trained on the ImageNet database, to characterize the overall appearance, heterogeneity of voxel values and heterogeneity of shape of Lung Nodules, respectively, and jointly utilizes them to classify Lung Nodules with an adaptive weighting scheme learned during the error back propagation. Experimental results on the benchmark LIDC-IDRI dataset show that our proposed TMME algorithm achieves a Lung Nodule classification accuracy of 93.40%, which is markedly higher than the accuracy of seven state-of-the-art approaches.
