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Brain Tumor Detection and Segmentation using Deep Learning
2021
International Journal of Computer Applications
Preserved Fulltext
Magnetic Resonance Imaging (MRI), Computerized Tomography (CT) scans, and Ultrasound images are generally used to get the brain images. ...
This work demonstrates Deep Learning"s potential in processing and extracting information from MRI images to provide a non-invasive tool for automated tumor detection and segmentation for clinical applications ...
The MRI images correspond to 110 patients included in The Cancer Genome Atlas (TCGA) lower-grade glioma collection with FLAIR modality i.e. fluid-attenuated inversion recovery sequence and genomic cluster ...
doi:10.5120/ijca2021921783
fatcat:unokubvqvzd53m4rfcx5dwxhhy
A Deep Multi-Task Learning Framework for Brain Tumor Segmentation
2021
Frontiers in Oncology
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In order to combine the different tasks of the two decoders, we weighted and added their corresponding loss functions, where the distance map prediction regularized the mask prediction. ...
With the development of deep learning, medical image segmentation is gradually automated. ...
METHODS The task of this paper is to segment brain tumors from threedimensional MRI images. ...
doi:10.3389/fonc.2021.690244
pmid:34150660
pmcid:PMC8212784
fatcat:z6qi27vfybbf3arvadbxertnaa
Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images
[article]
2022
arXiv
pre-print
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Semantic segmentation of brain tumors is a fundamental medical image analysis task involving multiple MRI imaging modalities that can assist clinicians in diagnosing the patient and successively studying ...
The swin transformer encoder extracts features at five different resolutions by utilizing shifted windows for computing self-attention and is connected to an FCNN-based decoder at each resolution via skip ...
The input to our model is 3D multi-modal MRI images with 4 channels. ...
arXiv:2201.01266v1
fatcat:kst2ddakorayfl4zfpimuacspy
ME-Net: Multi-Encoder Net Framework for Brain Tumor Segmentation
[article]
2022
arXiv
pre-print
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Aiming at segmenting 3D MRI, we propose a model for brain tumor segmentation with multiple encoders. The structure contains four encoders and one decoder. ...
The four encoders correspond to the four modalities of the MRI image, perform one-to-one feature extraction, and then merge the feature maps of the four modalities into the decoder. ...
Based on this, we propose a novel network structure with four encoders called Multi-Encoder Net (ME-Net) for the four modalities of MRI images. ...
arXiv:2203.11213v1
fatcat:3xlwng53rzhgpmjcujnpnlrwcu
ME‐Net : Multi‐encoder net framework for brain tumor segmentation
2021
International journal of imaging systems and technology (Print)
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Aiming at segmenting 3D MRI, we propose a model for brain tumor segmentation with multiple encoders. Our model reduces the difficulty of feature extraction and greatly improves model performance. ...
MRI plays a vital role to evaluate brain tumor diagnosis and treatment planning. However, the manual segmentation of the MRI image is strenuous. ...
ACKNOWLEDGMENTS This work is funded in part by the National Natural Science Foundation of China (Grant No. 62072413), and also supported in part by the AI for Health Imaging Award "CHAIMELEON: Accelerating ...
doi:10.1002/ima.22571
fatcat:3odtpaf5r5hgzj34oufonthnsq
Medical Image Segmentation with 3D Convolutional Neural Networks: A Survey
[article]
2022
arXiv
pre-print
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In addition, with the rapid advancements in three-dimensional (3D) imaging systems and the availability of excellent hardware and software support to process large volumes of data, 3D deep learning methods ...
are gaining popularity in medical image analysis. ...
In [48] , Kayalibay et al. proposed another 3D encoder-decoder architecture with deep supervision [49] . ...
arXiv:2108.08467v3
fatcat:s2rzghycjbczpparmrflsdzujq
StRegA: Unsupervised Anomaly Detection in Brain MRIs using a Compact Context-encoding Variational Autoencoder
[article]
2022
arXiv
pre-print
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This research proposes a compact version of the "context-encoding" VAE (ceVAE) model, combined with pre and post-processing steps, creating a UAD pipeline (StRegA), which is more robust on clinical data ...
, and shows its applicability in detecting anomalies such as tumours in brain MRIs. ...
SSAE architecture is an encoder-decoder network that aims to localise anomalies from reconstruction residuals. ...
arXiv:2201.13271v2
fatcat:uvvhr4ydnrejvngcwjkrv34zui
Brain Tumor Segmentation Based on Refined Fully Convolutional Neural Networks with A Hierarchical Dice Loss
[article]
2018
arXiv
pre-print
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Besides the structure modification, we also propose a new classifier with a hierarchical dice loss. ...
As a basic task in computer vision, semantic segmentation can provide fundamental information for object detection and instance segmentation to help the artificial intelligence better understand real world ...
From left to right: T1-weighted MRI (T1), T1-weighted MRI with gadolinium contrast enhancement (T1c), T2-weighted MRI (T2) and Fluid Attenuated Inversion Recovery (FLAIR). ...
arXiv:1712.09093v3
fatcat:x2shelg6crdshggbex3bjet7yi
A Review of Artificial Intelligence Technologies for Early Prediction of Alzheimer's Disease
[article]
2020
arXiv
pre-print
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The reliable and effective evaluation of early dementia has become essential research with medical imaging technologies and computer-aided algorithms. ...
This trend has moved to modern Artificial Intelligence (AI) technologies motivated by deeplearning success in image classification and natural language processing. ...
The MRI was preprocessed to segment Grey Matter (GM) and White Matter (WM) regions. DCA was composed of an encoder and a decoder. ...
arXiv:2101.01781v1
fatcat:lqtovw4jlbdcjhh5rvresp2gmq
Prior Attention Network for Multi-Lesion Segmentation in Medical Images
[article]
2021
arXiv
pre-print
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The accurate segmentation of multiple types of lesions from adjacent tissues in medical images is significant in clinical practice. ...
Experimental results show that in both 2D and 3D segmentation tasks our proposed network achieves better performance with less computational cost compared with cascaded networks. ...
Such algorithms typically feature a deep encoder to extract features automatically from the input images and the following operations to generate dense predictions. For example, Long et al. ...
arXiv:2110.04735v1
fatcat:racznl4ddvbdbcsx4ir6dse66e
Predicting Fluid Intelligence via Naturalistic Functional Connectivity Using Weighted Ensemble Model and Network Analysis
2021
NeuroSci
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intelligence prediction. ...
The functional connectivity and graphical indices computed using the preprocessed fMRI data were then all fed into an auto-encoder parallelly for automatic feature extraction to predict the fluid intelligence ...
extraction to predict the fluid intelligence. ...
doi:10.3390/neurosci2040032
fatcat:375rup7cvzc3rgxycxunfygdsa
Which Contrast Does Matter? Towards a Deep Understanding of MR Contrast using Collaborative GAN
[article]
2019
arXiv
pre-print
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Thanks to the recent success of generative adversarial network (GAN) for image synthesis, there are many exciting GAN approaches that successfully synthesize MR image contrast from other images with different ...
Our experimental results showed that the exogenous contrast from contrast agents is not replaceable, but other endogenous contrast such as T1, T2, etc can be synthesized from other contrast. ...
The decoder utilised the same blocks in the encoder, but with a single block per each spatial level. The other branch of the decoder is for the regularization. ...
arXiv:1905.04105v1
fatcat:tcldznehefeojp7tkc7ipld6f4
Deep Learning in Bioinformatics
[article]
2016
arXiv
pre-print
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Accordingly, application of deep learning in bioinformatics to gain insight from data has been emphasized in both academia and industry. ...
To provide a useful and comprehensive perspective, we categorize research both by the bioinformatics domain (i.e., omics, biomedical imaging, biomedical signal processing) and deep learning architecture ...
[124] studied Alzheimer's disease classification using cerebrospinal fluid and brain images in the forms of MRI and PET scan and Soleymani et al. ...
arXiv:1603.06430v5
fatcat:xvgg7misrrcsxmshty2emnujaq
Tumor Grade and Overall Survival Prediction of Gliomas Using Radiomics
2021
Scientific Programming
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Recently, various machine learning-based approaches have been developed to predict the prognosis based on preoperative magnetic resonance imaging (MRI) radiomics, which extract quantitative features from ...
vs. lower-grade gliomas) from preoperative MRI scans and (ii) prediction of patient overall survival (OS) in higher-grade gliomas (<12 months vs. > 12 months) from preoperative MRI scans. ...
) and imaging modality (MRI). ...
doi:10.1155/2021/9913466
doaj:aef25cf87c0b430ab470541cfacd2ed3
fatcat:zfj6r6aqcnehzcwzxvagzjhpgi
Novel Volumetric Sub-region Segmentation in Brain Tumors
2020
Frontiers in Computational Neuroscience
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An encoder-decoder type CNN model is designed for pixel-wise segmentation of the tumor along three anatomical planes (axial, sagittal, and coronal) at the slice level. ...
multi-modal MR images of the brain. ...
Segnet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. ...
doi:10.3389/fncom.2020.00003
pmid:32038216
pmcid:PMC6993215
fatcat:7clvk46kz5cptbwtusucyh76fm
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