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We performed this research to 1) evaluate a novel deep learning method for the diagnosis of Alzheimer's disease (AD) and 2) jointly predict the Mini Mental State Examination (MMSE) scores of South Korean patients with AD. Using resting-state functional Magnetic Resonance Imaging (rs-fMRI) scans of 331 participants, we obtained functional 3-dimensional (3-D) independent component spatial maps for use as features in classification and regression tasks. A 3-D convolutional neural network (CNN) architecture was developed for the classification task. MMSE scores were predicted using: linear least square regression (LLSR), support vector regression, bagging-based ensemble regression, and tree regression with group independent component analysis (gICA) features. To improve MMSE regression performance, we applied feature optimization methods including least absolute shrinkage and selection operator and support vector machine-based recursive feature elimination (SVM-RFE). The mean balanced test accuracy was 85.27% for the classification of AD versus healthy controls. The medial visual, default mode, dorsal attention, executive, and auditory related networks were mainly associated with AD. The maximum clinical MMSE score prediction accuracy with the LLSR method applied on gICA combined with SVM-RFE features had the lowest root mean square error (3.27 ± 0.58) and the highest R 2 value (0.63 ± 0.02). Classification of AD and healthy controls can be successfully achieved using only rs-fMRI and MMSE scores can be accurately predicted using functional independent component features. In the absence of trained clinicians, AD disease status and clinical MMSE scores can be jointly predicted using 3-D deep learning and regression learning approaches with rs-fMRI data.

Motivation: This study reports a framework to discriminate patients with schizophrenia and normal healthy control subjects, based on magnetic resonance imaging (MRI) of the brain. Resting-state functional MRI data from a total of 144 subjects (72 patients with schizophrenia and 72 healthy controls) was obtained from a publicly available dataset using a three-dimensional convolution neural network 3D-CNN based deep learning classification framework and ICA based features., Results: We achieved 98.09 ± 1.01% ten-fold cross-validated classification accuracy with a p-value < 0.001 and an area under the curve (AUC) of 0.9982 ± 0.015. In addition, differences in functional connectivity between the two groups were statistically analyzed across multiple resting-state networks. The disconnection between the visual and frontal network was prominent in patients, while they showed higher connectivity between the default mode network and other task-positive/ cerebellar networks. These ICA functional network maps served as highly discriminative three-dimensional imaging features for the discrimination of schizophrenia in this study., Conclusion: Due to the very high AUC, this research with more validation on the cross diagnosis and publicly available dataset, may be translated in future as an adjunct tool to assist clinicians in the initial screening of schizophrenia., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Different modalities such as structural MRI, FDG-PET, and CSF have complementary information, which is likely to be very useful for diagnosis of AD and MCI. Therefore, it is possible to develop a more effective and accurate AD/MCI automatic diagnosis method by integrating complementary information of different modalities. In this paper, we propose multi-modal sparse hierarchical extreme leaning machine (MSH-ELM). We used volume and mean intensity extracted from 93 regions of interest (ROIs) as features of MRI and FDG-PET, respectively, and used p-tau, t-tau, and A β 42 as CSF features. In detail, high-level representation was individually extracted from each of MRI, FDG-PET, and CSF using a stacked sparse extreme learning machine auto-encoder (sELM-AE). Then, another stacked sELM-AE was devised to acquire a joint hierarchical feature representation by fusing the high-level representations obtained from each modality. Finally, we classified joint hierarchical feature representation using a kernel-based extreme learning machine (KELM). The results of MSH-ELM were compared with those of conventional ELM, single kernel support vector machine (SK-SVM), multiple kernel support vector machine (MK-SVM) and stacked auto-encoder (SAE). Performance was evaluated through 10-fold cross-validation. In the classification of AD vs. HC and MCI vs. HC problem, the proposed MSH-ELM method showed mean balanced accuracies of 96.10% and 86.46%, respectively, which is much better than those of competing methods. In summary, the proposed algorithm exhibits consistently better performance than SK-SVM, ELM, MK-SVM and SAE in the two binary classification problems (AD vs. HC and MCI vs. HC)., (© 2018 Wiley Periodicals, Inc.)

This study aimed to improve the performance of sleep-stage classifiers by calculating not only electroencephalogram (EEG) patterns that appear in each sleep stage, but also the flow of information between brain regions. Methods: A continuous wavelet transform was used to determine brain activity, and a directed transfer function (DTF) was used to quantify the flow of information between brain regions. In addition, a multimodal architecture with a residual unit for improving the vanishing problem in deep learning and a residual attention network for localizing the information of input values were used to improve the efficiency and performance of sleep-stage classifiers. Results: The classification accuracy was 87.34%, F1-score was 87.42, and Cohen's kappa was 0.83. Further, the DTF values were analyzed using various approaches to identify brain connectivity based on the sleep stages. Conclusion: The accuracy, F1-score, and Cohen's kappa values confirmed a significant performance improvement compared with those of previously proposed sleep-stage classifiers. The results indicated that there were parts with significant differences in information flow based on the sleep stage, and that the DTF influenced the performance of sleep-stage classifiers by accurately reflecting the sleep stages. Significance: In previous studies, deep and machine learning were used to construct classifiers by utilizing significant changes in brain waves measured during sleep states. However, this study aimed to classify sleep stages more accurately by incorporating the flow of information between brain regions. The proposed classifier overcame these limitations and achieved superior results. [ABSTRACT FROM AUTHOR]

Preoperative chemoradiotherapy is known to reduce the local recurrence of locally advanced rectal cancer. However, the careful use of preoperative chemoradiotherapy is essential, because unnecessary over‐treatment can result in unintended complications. Therefore, a diagnostic system for distinguishing between T2 and T3 rectal cancers should be developed. According to the diagnostic criteria for rectal cancer, radiologists first identify the locations and the shapes of both the tumor and the rectum from a medical image and then diagnose the T2/T3 rectal cancer by determining whether the tumor passes through the rectal wall or not. We construct two distinct convolutional neural network models to achieve the automated segmentation of each rectum and tumor, respectively. Then, we construct another convolutional neural network model, which uses the output images of segmentation models as input and determines whether the tumor in the input magnetic resonance image is at the T2 stage or at the T3 stage. We evaluate the effectiveness of the proposed method based on 290 magnetic resonance images from 133 subjects. The proposed model demonstrates an accuracy of 94%. [ABSTRACT FROM AUTHOR]

Purpose : To perform automatic assessment of dementia severity using a deep learning framework applied to resting-state functional magnetic resonance imaging (rs-fMRI) data. Method : We divided 133 Alzheimer's disease (AD) patients with clinical dementia rating (CDR) scores from 0.5 to 3 into two groups based on dementia severity; the groups with very mild/mild (CDR: 0.5–1) and moderate to severe (CDR: 2–3) dementia consisted of 77 and 56 subjects, respectively. We used rs-fMRI to extract functional connectivity features, calculated using independent component analysis (ICA), and performed automated severity classification with three-dimensional convolutional neural networks (3D-CNNs) based on deep learning. Results : The mean balanced classification accuracy was 0.923 ± 0.042 (p < 0.001) with a specificity of 0.946 ± 0.019 and sensitivity of 0.896 ± 0.077. The rs-fMRI data indicated that the medial frontal, sensorimotor, executive control, dorsal attention, and visual related networks mainly correlated with dementia severity. Conclusions : Our CDR-based novel classification using rs-fMRI is an acceptable objective severity indicator. In the absence of trained neuropsychologists, dementia severity can be objectively and accurately classified using a 3D-deep learning framework with rs-fMRI independent components. [ABSTRACT FROM AUTHOR]

Cancer and its subtypes constitute approximately 30% of all causes of death globally and display a wide range of heterogeneity in terms of clinical and molecular responses to therapy. Molecular subtyping has enabled the use of precision medicine to overcome these challenges and provide significant biological insights to predict prognosis and improve clinical decision-making. Over the past decade, conventional machine learning (ML) and deep learning (DL) algorithms have been widely espoused for the classification of cancer subtypes from gene expression datasets. However, these methods are potentially biased toward the identification of cancer biomarkers. Hence, an end-to-end deep learning approach, DeepGene Transformer, is proposed which addresses the complexity of high-dimensional gene expression with a multi-head self-attention module by identifying relevant biomarkers across multiple cancer subtypes without requiring feature selection as a pre-requisite for the current classification algorithms. Comparative analysis reveals that the proposed DeepGene Transformer outperformed the commonly used traditional and state-of-the-art classification algorithms and can be considered an efficient approach for classifying cancer and its subtypes, indicating that any improvement in deep learning models in computational biologists can be reflected well in this domain as well. • A Transformer-based framework for accurate classification of cancer and its subtypes. • Representations learned from attention mechanism significantly increase accuracies. • DeepGene Transformer unveils distinct patterns in gene expressions and makes predictions. • Effectiveness of multi-head self-attention layer as a convolution layer is evaluated. • Proposed DeepGene Transformer is superior to current algorithms for automatic diagnosis. [ABSTRACT FROM AUTHOR]

Crowd counting is a challenging task due to large perspective, density, and scale variations. CNN-based crowd counting techniques have achieved significant performance in sparse to dense environments. However, crowd counting in high perspective-varying scenes (images) is getting harder due to different density levels occupied by the same number of pixels. In this way large variations for objects in the same spatial area make it difficult to count accurately. Further, existing CNN-based crowd counting methods are used to extract rich deep features; however, these features are used locally and disseminated while propagating through intermediate layers. This results in high counting errors, especially in dense and high perspective-variation scenes. Further, class-specific responses along channel dimensions are underestimated. To address these above mentioned issues, we therefore propose a CNN-based dense feature extraction network for accurate crowd counting. Our proposed model comprises three main modules: (1) backbone network, (2) dense feature extraction modules (DFEMs), and (3) channel attention module (CAM). The backbone network is used to obtain general features with strong transfer learning ability. The DFEM is composed of multiple sub-modules called dense stacked convolution modules (DSCMs), densely connected with each other. In this way features extracted from lower and middle-lower layers are propagated to higher layers through dense connections. In addition, combinations of task independent general features obtained by the former modules and task-specific features obtained by later ones are incorporated to obtain high counting accuracy in large perspective-varying scenes. Further, to exploit the class-specific response between background and foreground, CAM is incorporated at the end to obtain high-level features along channel dimensions for better counting accuracy. Moreover, we have evaluated the proposed method on three well known datasets: Shanghaitech (Part-A), Shanghaitech (Part-B), and Venice. The performance of the proposed technique justifies its relative effectiveness in terms of selected performance compared to state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

• Deep learning for identifying papillary thyroid carcinomas and computing certainty. • Ensemble technique combines multiple deep CNNs to significantly increase accuracies. • GRAD-CAM method highlights dominant tissues for the discriminative decision-making. • Effectiveness of stain normalization methods on predictive performances is evaluated. • Proposed ensemble learning is superior to current models for automatic diagnosis. Accurately cytopathological diagnosis of Papillary Thyroid Carcinoma (PTC) is of importance for reducing costs and increasing efficiency of treatments. In this paper, we pursue that goal by introducing artificial intelligence (AI) for automatic classification of malignant PTC cell clusters from Fine Needle Aspiration Cytology (FNAC) processed by ThinPrep. High-resolution cytological images obtained with a 40 × objective lens digital camera attached to an Olympus microscope were segmented into fragments and then divided into training, validation, and testing subsets. Fragments are non-overlapped patches containing only regions-of-interest that cover informative tissue structures for making proper diagnoses. Deep learning CNN models were pre-trained and fine-tuned on large-scale ImageNet domain before they were re-trained on cytology fragments. Moreover, we proposed a method to compute certainty of the patient-level prediction that undoubtedly provides additional evidence for reliability and confidence of the prediction. Results showed that the best classification performance on digital FNAC images achieved using DenseNet161, obtaining a mean accuracy of 0.9556 (p < 0.01), a mean sensitivity of 0.9734, and a mean specificity of 0.9405 on yet-to-be-seen test-set. Ensemble learning findings suggested combinations of AdaBoost classifier with multiple CNN models boosted predictive performances, up to 0.9971 accuracy. Moreover, stain normalization introduced by Reinhard increased the predictive ability, outperforming histogram specification, and Macenko methods. Presented findings demonstrate deep learning can integrate into computer-aided diagnosis systems to support cytopathologists in accurate diagnosis of PTC. [ABSTRACT FROM AUTHOR]