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Semi-supervised Learning using Denoising Autoencoders for Brain Lesion Detection and Segmentation
[article]
2017
arXiv
pre-print
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The ND learns the non-lesion brain accurately as it was also shown to provide good segmentation performance on ischemic brain lesions in images from a different database. ...
The weakly supervised SDAE (for HGG) and transfer learning based LGG network were also shown to generalize well and provide good segmentation on unseen BraTS 2013 & BraTS 2015 test data. ...
INTRODUCTION This paper Gliomas are a type of primary brain tumor that affect the glial cells in the brain. Based on severity, gliomas are further divided to HGG and LGG. ...
arXiv:1611.08664v4
fatcat:cvpduqai3nhtlca2e4t5euofbu
Deep Learning in Multi-organ Segmentation
[article]
2020
arXiv
pre-print
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This paper presents a review of deep learning (DL) in multi-organ segmentation. We summarized the latest DL-based methods for medical image segmentation and applications. ...
We provided a comprehensive comparison among DL-based methods for thoracic and head & neck multiorgan segmentation using benchmark datasets, including the 2017 AAPM Thoracic Auto-segmentation Challenge ...
[92] , which are multi-modality brain MRI tumor segmentation datasets, SDAE can provide good segmentation performance [2] . ...
arXiv:2001.10619v1
fatcat:6uwqwnzydzccblh5cajhsgdpea
Stacking denoising auto-encoders in a deep network to segment the brainstem on MRI in brain cancer patients: A clinical study
2016
Computerized Medical Imaging and Graphics
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To tackle these issues a deep learning approach based on stacking denoising auto-encoders has been proposed to segment the brainstem on magnetic resonance images in brain cancer context. ...
Additionally to classical features used in machine learning to segment brain structures, two new features are suggested. ...
Furthermore, instead of using patches from single or multi-modality MR images, we use hand-crafted features as input of the network. ...
doi:10.1016/j.compmedimag.2016.03.003
pmid:27236370
fatcat:xacgweyi2ne3tiffxicuwijopm
DG_talk.pdf
[article]
2017
Figshare
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Cases
Multi-modality Isointense Infant Brain Image Segmentation
Infant Brain Segmentation
Infant Brain Segmentation
W. ...
Shen, "Deep Convolutional neural networks for multi-
modality isointense infant brain image segmentation," Neuroimage, 2015. ...
doi:10.6084/m9.figshare.5254996.v2
fatcat:7qprrgc2mbgotpym3kyxvkmgxa
Deep Learning for Medical Image Analysis: Applications to Computed Tomography and Magnetic Resonance Imaging
2017
Hanyang Medical Reviews
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Specifically, analysis of advanced modalities such as computed tomography (CT) and magnetic resonance imaging (MRI) has benefited most from the data-driven nature of deep learning. ...
in medical images. ...
., a network architecture based on multi-scale T1 and FLAIR MR patches was proposed to segment and quantify white matter hyperintensities, which are related to various brain disorders [28] . ...
doi:10.7599/hmr.2017.37.2.61
fatcat:f4dl4szy35bhfilas3kyblzgui
Brain Tumor Analysis Empowered with Deep Learning: A Review, Taxonomy, and Future Challenges
2020
Brain Sciences
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Considering the wide range of applications of deep learning, the objective of this article is to review major deep learning concepts pertinent to brain tumor analysis (e.g., segmentation, classification ...
A review conducted by summarizing a large number of scientific contributions to the field (i.e., deep learning in brain tumor analysis) is presented in this study. ...
Multi-modal MRIs are used for brain tumor segmentation using automated generative models. ...
doi:10.3390/brainsci10020118
pmid:32098333
pmcid:PMC7071415
fatcat:wofq4puvcbemlconbz6carsf2y
Deep Learning in Cardiology
2019
IEEE Reviews in Biomedical Engineering
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2019 pre-print arXiv:1902.11122v1 fatcat:xeollgq57fb3zkwmxnm5uwo7kmOther Versions
In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. ...
Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. ...
[152] method is based on two FCN for multi-label whole heart localization and segmentation. ...
doi:10.1109/rbme.2018.2885714
fatcat:pa47trmskvflvig5cotth265q4
A survey on deep learning in medical image analysis
2017
Medical Image Analysis
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2017 pre-print arXiv:1702.05747v1 fatcat:zdedepkenraglidf7b4ojxcohiOther Versions
We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. ...
Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. ...
ArXiv was searched for papers mentioning one of a set of terms related to medical imaging. ...
doi:10.1016/j.media.2017.07.005
pmid:28778026
fatcat:esbj72ftwvbgzh6jgw367k73j4
A Comprehensive Survey on Deep-Learning-Based Breast Cancer Diagnosis
2021
Cancers
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Therefore, several researchers have established deep-learning-based automated methods for their efficiency and accuracy in predicting the growth of cancer cells utilizing medical imaging modalities. ...
Furthermore, a concrete review of diverse imaging modalities, performance metrics and results, challenges, and research directions for future researchers is presented. ...
Datasets can differ based on their imaging modality, class, and image format. The available datasets have distinct image formats. ...
doi:10.3390/cancers13236116
pmid:34885225
fatcat:ircywikuuvc25laiz3fsrc65bq
A Survey on Deep Learning for Precision Oncology
2022
Diagnostics
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Secondly, we provide an overview of the studies per anatomical area, including the brain, bladder, breast, bone, cervix, esophagus, gastric, head and neck, kidneys, liver, lung, pancreas, pelvis, prostate ...
[17] combined 2D and 3D U-Net for segmentation of metastatic brain tumors on MRI before and after radiotherapy. Hedden et al. ...
Finally, the DVH curve is reconstructed using DVH features based on SDAE. Ibragimov et al. ...
doi:10.3390/diagnostics12061489
pmid:35741298
pmcid:PMC9222056
fatcat:2qgvdz4x7rejxkwgoascxk77ke
Deep Learning in Medical Image Analysis
2017
Annual Review of Biomedical Engineering
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Specifically, exploiting hierarchical feature representations learned solely from data, instead of handcrafted features mostly designed based on domain-specific knowledge, lies at the core of the advances ...
On the image-understanding front, recent advances in machine learning, especially, in the way of deep learning, have made a big leap to help identify, classify, and quantify patterns in medical images. ...
(22) designed four CNN architectures to segment infant brain tissues based on multi-modality MR images. ...
doi:10.1146/annurev-bioeng-071516-044442
pmid:28301734
pmcid:PMC5479722
fatcat:amn6qgpt6fedzp3zejgi4aw66u
Computational biology: deep learning
2017
Emerging Topics in Life Sciences
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This exciting class of methods, based on artificial neural networks, quickly became popular due to its competitive performance in prediction problems. ...
In pioneering early work, applying simple network architectures to abundant data already provided gains over traditional counterparts in functional genomics, image analysis, and medical diagnostics. ...
Acknowledgements We thank Oliver Stegle for the comments on the text. ...
doi:10.1042/etls20160025
pmid:33525807
pmcid:PMC7289034
fatcat:qnw2yndsp5aqlnxxshtaipzctu
scSE-NL V-Net: A Brain Tumor Automatic Segmentation Method Based on Spatial and Channel "Squeeze-and-Excitation" Network With Non-local Block
2022
Frontiers in Neuroscience
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How to accurately and quickly segment brain tumor MRI images is still one of the hottest topics in the medical image analysis community. ...
Thereby, our model can make an auxiliary effect on the diagnosis of brain tumors established. ...
. • Spatial and channel "Squeeze and Excitation" used to strengthen the CNN network's image recognition ability. • Using multi-modal brain tumor segmentation challenge (BraTS) 2020 dataset and achieve ...
doi:10.3389/fnins.2022.916818
pmid:35712454
pmcid:PMC9197379
fatcat:qi6l7lwk3fbpvbwl2gz3n7cl5q
Artificial Neural Network Based Breast Cancer Screening: A Comprehensive Review
[article]
2020
arXiv
pre-print
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For predicting breast cancer, several automated systems are already developed using different medical imaging modalities. ...
This paper provides a systematic review of the literature on artificial neural network (ANN) based models for the diagnosis of breast cancer via mammography. ...
On the other hand, DNN based on ML-NN or CNN work well for high dimensional multicast BrC image datasets. ...
arXiv:2006.01767v1
fatcat:jjy3d2mgabfrrnpbkbyskfb2pi
A Brief Survey on Breast Cancer Diagnostic with Deep Learning Schemes Using Multi-Image Modalities
2020
IEEE Access
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This research focusses on providing benefits and risks of breast multi-imaging modalities, segmentation schemes, feature extraction, classification of breast abnormalities through state-of-the-art deep ...
However, several deep learning-based methods have been developed for reliable breast cancer prognosis and classification but very few of them provided a comprehensive overview of lesions segmentation. ...
Mahmoodet al.: A Brief Survey on Breast Cancer Diagnostic with Deep Learning Schemes Using Multi-Image Modalities 6% of the selected studies. ...
doi:10.1109/access.2020.3021343
fatcat:czvctyngmjg6bhzinpmrfmht64
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