Proceedings of ... International Joint Conference on Neural Networks. International Joint Conference on Neural Networks最新文献

Dynamic Perturbation of Weights for Improved Data Reconstruction in Unsupervised Learning. 无监督学习中改进数据重构的权值动态摄动。

Proceedings of ... International Joint Conference on Neural Networks. International Joint Conference on Neural Networks Pub Date : 2021-07-01 Epub Date: 2021-09-20 DOI: 10.1109/ijcnn52387.2021.9533539

Manar D Samad, Rahim Hossain, Khan M Iftekharuddin

{"title":"Dynamic Perturbation of Weights for Improved Data Reconstruction in Unsupervised Learning.","authors":"Manar D Samad, Rahim Hossain, Khan M Iftekharuddin","doi":"10.1109/ijcnn52387.2021.9533539","DOIUrl":"https://doi.org/10.1109/ijcnn52387.2021.9533539","url":null,"abstract":"The concept of weight pruning has shown success in neural network model compression with marginal loss in classification performance. However, similar concepts have not been well recognized in improving unsupervised learning. To the best of our knowledge, this paper proposes one of the first studies on weight pruning in unsupervised autoencoder models using non-imaging data points. We adapt the weight pruning concept to investigate the dynamic behavior of weights while reconstructing data using an autoencoder and propose a deterministic model perturbation algorithm based on the weight statistics. The model perturbation at periodic intervals resets a percentage of weight values using a binary weight mask. Experiments across eight non-imaging data sets ranging from gene sequence to swarm behavior data show that only a few periodic perturbations of weights improve the data reconstruction accuracy of autoencoders and additionally introduce model compression. All data sets yield a small portion of (<5%) weights that are substantially higher than the mean weight value. These weights are found to be much more informative than a substantial portion (>90%) of the weights with negative values. In general, the perturbation of low or negative weight values at periodic intervals has improved the data reconstruction loss for most data sets when compared to the case without perturbation. The proposed approach may help explain and correct the dynamic behavior of neural network models in a deterministic way for data reconstruction and obtaining a more accurate representation of latent variables using autoencoders.","PeriodicalId":89613,"journal":{"name":"Proceedings of ... International Joint Conference on Neural Networks. International Joint Conference on Neural Networks","volume":"2021 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9493331/pdf/nihms-1836374.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"33482468","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

DeepConsensus: Consensus-based Interpretable Deep Neural Networks with Application to Mortality Prediction. DeepConsensus：基于共识的可解释深度神经网络在死亡率预测中的应用。

Proceedings of ... International Joint Conference on Neural Networks. International Joint Conference on Neural Networks Pub Date : 2020-07-01 Epub Date: 2020-09-28 DOI: 10.1109/ijcnn48605.2020.9206678

Shaeke Salman, Seyedeh Neelufar Payrovnaziri, Xiuwen Liu, Pablo Rengifo-Moreno, Zhe He

{"title":"DeepConsensus: Consensus-based Interpretable Deep Neural Networks with Application to Mortality Prediction.","authors":"Shaeke Salman, Seyedeh Neelufar Payrovnaziri, Xiuwen Liu, Pablo Rengifo-Moreno, Zhe He","doi":"10.1109/ijcnn48605.2020.9206678","DOIUrl":"10.1109/ijcnn48605.2020.9206678","url":null,"abstract":"Deep neural networks have achieved remarkable success in various challenging tasks. However, the black-box nature of such networks is not acceptable to critical applications, such as healthcare. In particular, the existence of adversarial examples and their overgeneralization to irrelevant, out-of-distribution inputs with high confidence makes it difficult, if not impossible, to explain decisions by such networks. In this paper, we analyze the underlying mechanism of generalization of deep neural networks and propose an (n, k) consensus algorithm which is insensitive to adversarial examples and can reliably reject out-of-distribution samples. Furthermore, the consensus algorithm is able to improve classification accuracy by using multiple trained deep neural networks. To handle the complexity of deep neural networks, we cluster linear approximations of individual models and identify highly correlated clusters among different models to capture feature importance robustly, resulting in improved interpretability. Motivated by the importance of building accurate and interpretable prediction models for healthcare, our experimental results on an ICU dataset show the effectiveness of our algorithm in enhancing both the prediction accuracy and the interpretability of deep neural network models on one-year patient mortality prediction. In particular, while the proposed method maintains similar interpretability as conventional shallow models such as logistic regression, it improves the prediction accuracy significantly.","PeriodicalId":89613,"journal":{"name":"Proceedings of ... International Joint Conference on Neural Networks. International Joint Conference on Neural Networks","volume":"2020 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7583142/pdf/nihms-1634785.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38526735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Representation of Deep Features using Radiologist defined Semantic Features. 使用放射科医师定义的语义特征表示深度特征。

Proceedings of ... International Joint Conference on Neural Networks. International Joint Conference on Neural Networks Pub Date : 2018-07-01 Epub Date: 2018-09-15 DOI: 10.1109/IJCNN.2018.8489440

Rahul Paul, Ying Liu, Qian Li, Lawrence Hall, Dmitry Goldgof, Yoganand Balagurunathan, Matthew Schabath, Robert Gillies

{"title":"Representation of Deep Features using Radiologist defined Semantic Features.","authors":"Rahul Paul, Ying Liu, Qian Li, Lawrence Hall, Dmitry Goldgof, Yoganand Balagurunathan, Matthew Schabath, Robert Gillies","doi":"10.1109/IJCNN.2018.8489440","DOIUrl":"https://doi.org/10.1109/IJCNN.2018.8489440","url":null,"abstract":"Semantic features are common radiological traits used to characterize a lesion by a trained radiologist. These features have been recently formulated, quantified on a point scale in the context of lung nodules by our group. Certain radiological semantic traits have been shown to extremely predictive of malignancy [26]. Semantic traits observed by a radiologist at examination describe the nodules and the morphology of the lung nodule shape, size, border, attachment to vessel or pleural wall, location and texture etc. Deep features are numeric descriptors often obtained from a convolutional neural network (CNN) which are widely used for classification and recognition. Deep features may contain information about texture and shape, primarily. Lately, with the advancement of deep learning, convolutional neural networks (CNN) are also being used to analyze lung nodules. In this study, we relate deep features to semantic features by looking for similarity in ability to classify. Deep features were obtained using a transfer learning approach from both an ImageNet pre-trained CNN and our trained CNN architecture. We found that some of the semantic features can be represented by one or more deep features. In this process, we can infer that some deep feature(s) have similar discriminatory ability as semantic features.","PeriodicalId":89613,"journal":{"name":"Proceedings of ... International Joint Conference on Neural Networks. International Joint Conference on Neural Networks","volume":"2018 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/IJCNN.2018.8489440","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36674768","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Predicting Nodule Malignancy using a CNN Ensemble Approach. 使用CNN集合方法预测结节性恶性肿瘤。

Proceedings of ... International Joint Conference on Neural Networks. International Joint Conference on Neural Networks Pub Date : 2018-07-01 Epub Date: 2018-10-15 DOI: 10.1109/IJCNN.2018.8489345

Rahul Paul, Lawrence Hall, Dmitry Goldgof, Matthew Schabath, Robert Gillies

{"title":"Predicting Nodule Malignancy using a CNN Ensemble Approach.","authors":"Rahul Paul, Lawrence Hall, Dmitry Goldgof, Matthew Schabath, Robert Gillies","doi":"10.1109/IJCNN.2018.8489345","DOIUrl":"10.1109/IJCNN.2018.8489345","url":null,"abstract":"Lung cancer is the leading cause of cancer-related deaths globally, which makes early detection and diagnosis a high priority. Computed tomography (CT) is the method of choice for early detection and diagnosis of lung cancer. Radiomics features extracted from CT-detected lung nodules provide a good platform for early detection, diagnosis, and prognosis. In particular when using low dose CT for lung cancer screening, effective use of radiomics can yield a precise non-invasive approach to nodule tracking. Lately, with the advancement of deep learning, convolutional neural networks (CNN) are also being used to analyze lung nodules. In this study, our own trained CNNs, a pre-trained CNN and radiomics features were used for predictive analysis. Using subsets of participants from the National Lung Screening Trial, we investigated if the prediction of nodule malignancy could be further enhanced by an ensemble of classifiers using different feature sets and learning approaches. We extracted probability predictions from our different models on an unseen test set and combined them to generate better predictions. Ensembles were able to yield increased accuracy and area under the receiver operating characteristic curve (AUC). The best-known AUC of 0.96 and accuracy of 89.45% were obtained, which are significant improvements over the previous best AUC of 0.87 and accuracy of 76.79%.","PeriodicalId":89613,"journal":{"name":"Proceedings of ... International Joint Conference on Neural Networks. International Joint Conference on Neural Networks","volume":"2018 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/IJCNN.2018.8489345","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36674769","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 40

Subject-Specific Convolutional Neural Networks for Accelerated Magnetic Resonance Imaging. 用于加速磁共振成像的特定对象卷积神经网络

Proceedings of ... International Joint Conference on Neural Networks. International Joint Conference on Neural Networks Pub Date : 2018-07-01 Epub Date: 2018-10-15 DOI: 10.1109/IJCNN.2018.8489393

Mehmet Akçakay, Steen Moeller, Sebastian Weingärtner, Kâmil Uğurbil

{"title":"Subject-Specific Convolutional Neural Networks for Accelerated Magnetic Resonance Imaging.","authors":"Mehmet Akçakay, Steen Moeller, Sebastian Weingärtner, Kâmil Uğurbil","doi":"10.1109/IJCNN.2018.8489393","DOIUrl":"10.1109/IJCNN.2018.8489393","url":null,"abstract":"Magnetic Resonance Imaging (MRI) is one of the leading modalities for medical imaging, providing excellent soft-tissue contrast without exposure to ionizing radiation. Despite continuing advances in MRI, long scan times remain a major limitation in clinical applications. Parallel imaging is a technique for scan time acceleration in MRI, which utilizes the spatial variations in the reception profiles of receiver coil arrays to reconstruct images from undersampled Fourier space, i.e. k-space. One of the most commonly used parallel imaging techniques employs interpolation of missing k-space information by using linear shift-invariant convolutional kernels. These kernels are trained on a limited amount of autocalibration signal (ACS) for each scan. We propose a novel method for parallel imaging, Robust Artificial-neural-networks for k-space Interpolation (RAKI), which uses scan-specific convolutional neural networks (CNNs) to perform improved k-space interpolation. Three-layer CNNs are trained using only scan-specific ACS data, alleviating the need for large training databases. The proposed method was tested in ultra-high resolution brain MRI and quantitative cardiac MRI, acquired with various acceleration rates. Improved noise resilience as compared to existing parallel imaging methods was observed for high acceleration rates or in the presence of low signal-to-noise ratio (SNR). Furthermore, RAKI successfully reconstructed images for quantitative cardiac MRI, even when using the same CNN across images with varying contrasts. These results indicate that RAKI achieves improved noise performance without overfitting to specific image contents, and offers great promise for improved acceleration in a wide range of MRI applications.","PeriodicalId":89613,"journal":{"name":"Proceedings of ... International Joint Conference on Neural Networks. International Joint Conference on Neural Networks","volume":"2018 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6938221/pdf/nihms-1064589.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37504654","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Classification Based on Neuroimaging Data by Tensor Boosting. 基于张量增强的神经影像数据分类。

Proceedings of ... International Joint Conference on Neural Networks. International Joint Conference on Neural Networks Pub Date : 2017-05-01 Epub Date: 2017-07-03 DOI: 10.1109/ijcnn.2017.7965985

Bo Zhang, Hua Zhou, Liwei Wang, Chul Sung

引用次数: 5

DL-PRO: A Novel Deep Learning Method for Protein Model Quality Assessment. DL-PRO:一种新的蛋白质模型质量评估的深度学习方法。

Proceedings of ... International Joint Conference on Neural Networks. International Joint Conference on Neural Networks Pub Date : 2014-07-01 DOI: 10.1109/IJCNN.2014.6889891

Son P Nguyen, Yi Shang, Dong Xu

{"title":"DL-PRO: A Novel Deep Learning Method for Protein Model Quality Assessment.","authors":"Son P Nguyen, Yi Shang, Dong Xu","doi":"10.1109/IJCNN.2014.6889891","DOIUrl":"https://doi.org/10.1109/IJCNN.2014.6889891","url":null,"abstract":"Computational protein structure prediction is very important for many applications in bioinformatics. In the process of predicting protein structures, it is essential to accurately assess the quality of generated models. Although many single-model quality assessment (QA) methods have been developed, their accuracy is not high enough for most real applications. In this paper, a new approach based on C-α atoms distance matrix and machine learning methods is proposed for single-model QA and the identification of native-like models. Different from existing energy/scoring functions and consensus approaches, this new approach is purely geometry based. Furthermore, a novel algorithm based on deep learning techniques, called DL-Pro, is proposed. For a protein model, DL-Pro uses its distance matrix that contains pairwise distances between two residues' C-α atoms in the model, which sometimes is also called contact map, as an orientation-independent representation. From training examples of distance matrices corresponding to good and bad models, DL-Pro learns a stacked autoencoder network as a classifier. In experiments on selected targets from the Critical Assessment of Structure Prediction (CASP) competition, DL-Pro obtained promising results, outperforming state-of-the-art energy/scoring functions, including OPUS-CA, DOPE, DFIRE, and RW.","PeriodicalId":89613,"journal":{"name":"Proceedings of ... International Joint Conference on Neural Networks. International Joint Conference on Neural Networks","volume":"2014 ","pages":"2071-2078"},"PeriodicalIF":0.0,"publicationDate":"2014-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/IJCNN.2014.6889891","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"32811004","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 46

Machine learning to predict extubation outcome in premature infants. 机器学习预测早产儿拔管结果。

Proceedings of ... International Joint Conference on Neural Networks. International Joint Conference on Neural Networks Pub Date : 2013-08-01 DOI: 10.1109/IJCNN.2013.6707058

Martina Mueller, Carol C Wagner, Romesh Stanislaus, Jonas S Almeida

{"title":"Machine learning to predict extubation outcome in premature infants.","authors":"Martina Mueller, Carol C Wagner, Romesh Stanislaus, Jonas S Almeida","doi":"10.1109/IJCNN.2013.6707058","DOIUrl":"https://doi.org/10.1109/IJCNN.2013.6707058","url":null,"abstract":"Though treatment of the ventilated premature infant has experienced many advances over the past decades, determining the best time point for extubation of these infants remains challenging and the incidence of extubation failures largely unchanged. The objective was to provide clinicians with a decision-support tool to determine whether to extubate a mechanically ventilated premature infant by using a set of machine learning algorithms on a dataset assembled from 486 premature infants receiving mechanical ventilation. Algorithms included artificial neural networks (ANN), support vector machine (SVM), naïve Bayesian classifier (NBC), boosted decision trees (BDT), and multivariable logistic regression (MLR). Results for ANN, MLR, and NBC were satisfactory (area under the curve [AUC]: 0.63-0.76); however, SVM and BDT consistently showed poor performance (AUC ~0.5). Complex medical data such as the data set used for this study require further preprocessing steps before prediction models can be developed that achieve similar or better performance than clinicians.","PeriodicalId":89613,"journal":{"name":"Proceedings of ... International Joint Conference on Neural Networks. International Joint Conference on Neural Networks","volume":"2013 ","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2013-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/IJCNN.2013.6707058","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"32889033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 11

A Framework for Content-based Retrieval of EEG with Applications to Neuroscience and Beyond. 基于内容的脑电图检索框架，应用于神经科学及其他领域。

Proceedings of ... International Joint Conference on Neural Networks. International Joint Conference on Neural Networks Pub Date : 2013-01-01 DOI: 10.1109/IJCNN.2013.6707106

Kyungmin Su, Kay A Robbins

引用次数: 0

Magnetic Resonance Imaging Estimation of Longitudinal Relaxation Rate Change (ΔR₁) in Dual Gradient Echo Sequences Using an Adaptive Model. 基于自适应模型的双梯度回波序列纵向松弛速率变化的磁共振成像估计(ΔR1)。

Proceedings of ... International Joint Conference on Neural Networks. International Joint Conference on Neural Networks Pub Date : 2011-01-01 DOI: 10.1109/IJCNN.2011.6033544

H Bagher-Ebadian, S P Nejad-Davarani, M M Ali, S Brown, M Makki, Q Jiang, D C Noll, J R Ewing

{"title":"Magnetic Resonance Imaging Estimation of Longitudinal Relaxation Rate Change (ΔR1) in Dual Gradient Echo Sequences Using an Adaptive Model.","authors":"H Bagher-Ebadian, S P Nejad-Davarani, M M Ali, S Brown, M Makki, Q Jiang, D C Noll, J R Ewing","doi":"10.1109/IJCNN.2011.6033544","DOIUrl":"https://doi.org/10.1109/IJCNN.2011.6033544","url":null,"abstract":"Magnetic Resonance Imaging (MRI) estimation of contrast agent concentration in fast pulse sequences such as Dual Gradient Echo (DGE) imaging is challenging. An Adaptive Neural Network (ANN) was trained with a map of contrast agent concentration estimated by Look-Locker (LL) technique (modified version of inversion recovery imaging) as a gold standard. Using a set of features extracted from DGE MRI data, an ANN was trained to create a voxel based estimator of the time trace of CA concentration. The ANN was trained and tested with the DGE and LL information of six Fisher rats using a K-Fold Cross-Validation (KFCV) method with 60 folds and 10500 samples. The Area Under the Receiver Operator Characteristic Curve (AUROC) for 60 folds was used for training, testing and optimization of the ANN. After training and optimization, the optimal ANN (4:7:5:1) produced maps of CA concentration which were highly correlated (r =0.89, P < 0.0001) with the CA concentration estimated by the LL technique. The estimation made by the ANN had an excellent overall performance (AUROC = 0.870).","PeriodicalId":89613,"journal":{"name":"Proceedings of ... International Joint Conference on Neural Networks. International Joint Conference on Neural Networks","volume":"2011 ","pages":"2501-2506"},"PeriodicalIF":0.0,"publicationDate":"2011-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/IJCNN.2011.6033544","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"32722311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3