scholarly journals Investigating the Discrimination Ability of 3D Convolutional Neural Networks Applied to Altered Brain MRI Parametric Maps

2021 ◽  
Author(s):  
Giulia Maria Mattia ◽  
Federico Nemmi ◽  
Edouard Villain ◽  
Marie-Véronique Le Lann ◽  
Xavier Franceries ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Network Performance ◽  
Brain Mri ◽  
Brain Regions ◽  
Target Region ◽  
Discrimination Ability ◽  
Weighted Magnetic Resonance Imaging ◽  
The Brain ◽  
Parametric Maps

Convolutional neural networks are gradually being recognized in the neuroimaging community as a powerful tool for image analysis. In the present study, we tested the ability of 3D convolutional neural networks to discriminate between whole-brain parametric maps obtained from diffusion-weighted magnetic resonance imaging. Original parametric maps were subjected to intensity-based region-specific alterations, to create altered maps. To analyze how position, size and intensity of altered regions affected the networks’ learning process, we generated monoregion and biregion maps by systematically modifying the size and intensity of one or two brain regions in each image. We assessed network performance over a range of intensity increases and combinations of maps, carrying out 10-fold cross-validation and using a hold-out set for testing. We then tested the networks trained with monoregion images on the corresponding biregion images and vice versa. Results showed an inversely proportional link between size and intensity for the monoregion networks, in that the larger the region, the smaller the increase in intensity needed to achieve good performances. Accuracy was better for biregion networks than for their monoregion counterparts, showing that altering more than one region in the brain can improve discrimination. Monoregion networks correctly detected their target region in biregion maps, whereas biregion networks could only detect one of the two target regions at most. Biregion networks therefore learned a more complex pattern that was absent from the monoregion images. This deep learning approach could be tailored to explore the behavior of other convolutional neural networks for other regions of interest. <br>

scholarly journals Investigating the Discrimination Ability of 3D Convolutional Neural Networks Applied to Altered Brain MRI Parametric Maps

2021 ◽  
Author(s):  
Giulia Maria Mattia ◽  
Federico Nemmi ◽  
Edouard Villain ◽  
Marie-Véronique Le Lann ◽  
Xavier Franceries ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Network Performance ◽  
Brain Mri ◽  
Brain Regions ◽  
Target Region ◽  
Discrimination Ability ◽  
Weighted Magnetic Resonance Imaging ◽  
The Brain ◽  
Parametric Maps

Convolutional neural networks are gradually being recognized in the neuroimaging community as a powerful tool for image analysis. In the present study, we tested the ability of 3D convolutional neural networks to discriminate between whole-brain parametric maps obtained from diffusion-weighted magnetic resonance imaging. Original parametric maps were subjected to intensity-based region-specific alterations, to create altered maps. To analyze how position, size and intensity of altered regions affected the networks’ learning process, we generated monoregion and biregion maps by systematically modifying the size and intensity of one or two brain regions in each image. We assessed network performance over a range of intensity increases and combinations of maps, carrying out 10-fold cross-validation and using a hold-out set for testing. We then tested the networks trained with monoregion images on the corresponding biregion images and vice versa. Results showed an inversely proportional link between size and intensity for the monoregion networks, in that the larger the region, the smaller the increase in intensity needed to achieve good performances. Accuracy was better for biregion networks than for their monoregion counterparts, showing that altering more than one region in the brain can improve discrimination. Monoregion networks correctly detected their target region in biregion maps, whereas biregion networks could only detect one of the two target regions at most. Biregion networks therefore learned a more complex pattern that was absent from the monoregion images. This deep learning approach could be tailored to explore the behavior of other convolutional neural networks for other regions of interest. <br>

scholarly journals 3D convolutional neural networks for classification of Alzheimer’s and Parkinson’s disease with T1-weighted brain MRI

2021 ◽  
Author(s):  
Nikhil J. Dhinagar ◽  
Sophia I. Thomopoulos ◽  
Conor Owens-Walton ◽  
Dimitris Stripelis ◽  
Jose Luis Ambite ◽  
...  
Keyword(s):  
Neural Networks ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Convolutional Neural Networks ◽  
Brain Mri

scholarly journals Efficient implementation of convolutional neural networks in the data processing of two-photon in vivo imaging

2019 ◽  
Vol 35 (17) ◽  
pp. 3208-3210 ◽  
Author(s):  
Yangzhen Wang ◽  
Feng Su ◽  
Shanshan Wang ◽  
Chaojuan Yang ◽  
Yonglu Tian ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Detection Methods ◽  
Supplementary Information ◽  
Imaging Data ◽  
Two Photon ◽  
Processing Power ◽  
Fluctuation Method ◽  
The Brain

Abstract Motivation Functional imaging at single-neuron resolution offers a highly efficient tool for studying the functional connectomics in the brain. However, mainstream neuron-detection methods focus on either the morphologies or activities of neurons, which may lead to the extraction of incomplete information and which may heavily rely on the experience of the experimenters. Results We developed a convolutional neural networks and fluctuation method-based toolbox (ImageCN) to increase the processing power of calcium imaging data. To evaluate the performance of ImageCN, nine different imaging datasets were recorded from awake mouse brains. ImageCN demonstrated superior neuron-detection performance when compared with other algorithms. Furthermore, ImageCN does not require sophisticated training for users. Availability and implementation ImageCN is implemented in MATLAB. The source code and documentation are available at https://github.com/ZhangChenLab/ImageCN. Supplementary information Supplementary data are available at Bioinformatics online.

GLIA-Deep: Glioblastoma Image Analysis Using Deep Learning Convolutional Neural Networks to Accurately Classify Gene Methylation and Predict Drug Effectiveness

2020 ◽  
Author(s):  
Viraj Mehta
Keyword(s):  
Neural Networks ◽  
Dna Repair ◽  
Precision Medicine ◽  
Survival Time ◽  
Convolutional Neural Networks ◽  
Alkylating Agent ◽  
Brain Mri ◽  
Methylation Status ◽  
Chemotherapy Drugs ◽  
Mri Scans

Glioblastoma multiforme is a deadly brain cancer with a median patient survival time of 18-24 months, despite aggressive treatments. This limited success is due to a combination of aggressive tumor behavior, genetic heterogeneity of the disease within a single patient&rsquo;s tumor, resistance to therapy, and lack of precision medicine treatments. A single specimen using a biopsy cannot be used for complete assessment of the tumor&rsquo;s microenvironment, making personalized care limited and challenging. Temozolomide (TMZ) is a commercially approved alkylating agent used to treat glioblastoma, but around 50% of temozolomide-treated patients do not respond to it due to the over-expression of O6-methylguanine methyltransferase (MGMT). MGMT is a DNA repair enzyme that rescues tumor cells from alkylating agent-induced damage, leading to resistance to chemotherapy drugs. Epigenetic silencing of the MGMT gene by promoter methylation results in decreased MGMT protein expression, reduced DNA repair activity, increased sensitivity to TMZ, and longer survival time. Thus, it is paramount that clinicians determine the methylation status of patients to provide personalized chemotherapy drugs. However, current methods for determining this via invasive biopsies or manually curated features from brain MRI (Magnetic Resonance Imaging) scans are time- and cost- intensive, and have a very low accuracy. Authors present a novel approach of using convolutional neural networks to predict methylation status and recommend patient-specific treatments via an analysis of brain MRI scans. The authors have developed an AI platform, GLIA-Deep, using a U-Net architecture and a ResNet-50 architecture trained on genomic data from TCGA (The Cancer Genome Atlas through the National Cancer Institute) and brain MRI scans from TCIA (The Cancer Imaging Archive). GLIA-Deep performs tumor region identification and determines MGMT methylation status with 90% accuracy in less than 5 seconds, a real-time analysis that eliminates huge time and cost investments of invasive biopsies. Using computational modeling, the analysis further recommends microRNAs that modulate MGMT gene expression by translational repression to make glioma cells TMZ sensitive, thereby improving the survival of glioblastoma patients with unmethylated MGMT. GLIA-Deep is a completely integrated, end-to-end, cost-effective and time-efficient platform that advances precision medicine by recommending personalized therapies from an analysis of individual MRI scans to improving glioblastoma treatment options.

scholarly journals Orthogonal Representations of Object Shape and Category in Deep Convolutional Neural Networks and Human Visual Cortex

2019 ◽  
Author(s):  
Astrid A. Zeman ◽  
J. Brendan Ritchie ◽  
Stefania Bracci ◽  
Hans Op de Beeck
Keyword(s):  
Neural Networks ◽  
Visual Cortex ◽  
Convolutional Neural Networks ◽  
Network Performance ◽  
Temporal Cortex ◽  
Visual Object ◽  
Visual Object Recognition ◽  
Deep Convolutional Neural Networks ◽  
Shape Information ◽  
Category Information

AbstractDeep Convolutional Neural Networks (CNNs) are gaining traction as the benchmark model of visual object recognition, with performance now surpassing humans. While CNNs can accurately assign one image to potentially thousands of categories, network performance could be the result of layers that are tuned to represent the visual shape of objects, rather than object category, since both are often confounded in natural images. Using two stimulus sets that explicitly dissociate shape from category, we correlate these two types of information with each layer of multiple CNNs. We also compare CNN output with fMRI activation along the human visual ventral stream by correlating artificial with biological representations. We find that CNNs encode category information independently from shape, peaking at the final fully connected layer in all tested CNN architectures. Comparing CNNs with fMRI brain data, early visual cortex (V1) and early layers of CNNs encode shape information. Anterior ventral temporal cortex encodes category information, which correlates best with the final layer of CNNs. The interaction between shape and category that is found along the human visual ventral pathway is echoed in multiple deep networks. Our results suggest CNNs represent category information independently from shape, much like the human visual system.

scholarly journals Deep Learning in Brain MRI: Effect of Data Leakage Due to Slice-level Split Using 2D Convolutional Neural Networks

2021 ◽  
Author(s):  
Ekin Yagis ◽  
Selamawet Workalemahu Atnafu ◽  
Alba García Seco de Herrera ◽  
Chiara Marzi ◽  
Marco Giannelli ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Neural Networks ◽  
Alzheimer's Disease ◽  
Convolutional Neural Networks ◽  
De Novo ◽  
Neurological Diseases ◽  
Brain Mri ◽  
Progression Markers ◽  
Magnetic Resonance Imaging Mri

Abstract In recent years, 2D convolutional neural networks (CNNs) have been extensively used for the diagnosis of neurological diseases from magnetic resonance imaging (MRI) data due to their potential to discern subtle and intricate patterns. Despite the high performances reported in numerous studies, developing CNN models with good generalization abilities is still a challenging task due to possible data leakage introduced during cross-validation (CV). In this study, we quantitatively assessed the effect of a data leakage caused by 3D MRI data splitting based on a 2D slice-level using three 2D CNN models for the classification of patients with Alzheimer’s disease (AD) and Parkinson’s disease (PD). Our experiments showed that slice-level CV erroneously boosted the average slice level accuracy on the test set by 30% on Open Access Series of Imaging Studies (OASIS), 29% on Alzheimer’s Disease Neuroimaging Initiative (ADNI), 48% on Parkinson's Progression Markers Initiative (PPMI) and 55% on a local de-novo PD Versilia dataset. Further tests on a randomly labeled OASIS-derived dataset produced about 96% of (erroneous) accuracy (slice-level split) and 50% accuracy (subject-level split), as expected from a randomized experiment. Overall, the extent of the effect of an erroneous slice-based CV is severe, especially for small datasets.

scholarly journals Evaluation of EEG identification potential using statistical approach and convolutional neural networks

2020 ◽  
pp. 37-49
Author(s):  
Alexey Sulavko ◽  
Pavel Lozhnikov ◽  
Adil Choban ◽  
Denis Stadnikov ◽  
Alexey Nigrey ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Statistical Approach ◽  
Alcohol Intoxication ◽  
Individual Characteristics ◽  
Monitoring Applications ◽  
Psychophysiological State ◽  
The Individual ◽  
Personality Recognition ◽  
The Brain

Introduction: Electroencephalograms contain information about the individual characteristics of the brain activities and the psychophysiological state of a subject. Purpose: To evaluate the identification potential of EEG, and to develop methods for the identification of users, their psychophysiological states and activities performed on a computer by their EEGs using convolutional neural networks. Results: The information content of EEG rhythms was assessed from the viewpoint of the possibility to identify a person and his/her state. A high accuracy of determining the identity (98.5–99.99% for 10 electrodes, 96.47% for two electrodes Fp1 and Fp2) with a low transit time (2–2.5 s) was achieved. A significant decrease in accuracy was detected if the person was in different psychophysiological states during the training and testing. In earlier studies, this aspect was not given enough attention. A method is proposed for increasing the robustness of personality recognition in altered psychophysiological states. An accuracy of 82–94% was achieved in recognizing states of alcohol intoxication, drowsiness or physical fatigue, and of 77.8–98.72% in recognizing the user's activities (reading, typing or watching video). Practical relevance: The results can be applied in security and remote monitoring applications.

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