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Sparse coding of pathology slides compared to transfer learning with deep neural networks

Will Fischer, Sanketh S. Moudgalya, Judith D. Cohn, Nga T. T. Nguyen, Garrett T. Kenyon
2018 BMC Bioinformatics  
However, when all layers up to the last convolutional layer in RESNET are replaced with a single feature map inferred via a sparse coding using a dictionary optimized for sparse reconstruction of unlabeled  ...  an approach for learning features and inferring representations of cancer pathology slides based on sparse coding.  ...  Medical imagery has been a target of artificial intelligence since the 1970s, and the majority of current approaches are based on "Deep Learning" using convolutional neural networks (reviewed in [3, 4  ... 
doi:10.1186/s12859-018-2504-8 fatcat:v3hxmphlkjenfp5au67scjwc5y

Sparse Coding Driven Deep Decision Tree Ensembles for Nuclear Segmentation in Digital Pathology Images [article]

Jie Song, Liang Xiao, Mohsen Molaei, Zhichao Lian
2020 arXiv   pre-print
In this paper, we propose an easily trained yet powerful representation learning approach with performance highly competitive to deep neural networks in a digital pathology image segmentation task.  ...  several state-of-the-art deep learning methods such as convolutional neural networks (CNN), fully convolutional networks (FCN), etc.  ...  performance in pathology image segmentation.  ... 
arXiv:2008.05657v1 fatcat:izdgwawo2zdnzbjcmotfu6akzy

Stacked Sparse Autoencoder (SSAE) for Nuclei Detection on Breast Cancer Histopathology Images

Jun Xu, Lei Xiang, Qingshan Liu, Hannah Gilmore, Jianzhong Wu, Jinghai Tang, Anant Madabhushi
2016 IEEE Transactions on Medical Imaging  
However, automated nucleus detection is complicated by (1) the large number of nuclei and the size of high resolution digitized pathology images, and (2) the variability in size, shape, appearance, and  ...  In this paper, a Stacked Sparse Autoencoder (SSAE), an instance of a deep learning strategy, is presented for efficient nuclei detection on high-resolution histopathological images of breast cancer.  ...  Then the "decoder" network reconstructs the pixel intensities within the image patch via the s 1 dimensional feature.  ... 
doi:10.1109/tmi.2015.2458702 pmid:26208307 pmcid:PMC4729702 fatcat:xuz7swthcjanphpzhf7tqxcysu

Deep Adversarial Training for Multi-Organ Nuclei Segmentation in Histopathology Images [article]

Faisal Mahmood, Daniel Borders, Richard Chen, Gregory N. McKay, Kevan J. Salimian, Alexander Baras, Nicholas J. Durr
2018 arXiv   pre-print
Conventional vision-based methods for nuclei segmentation struggle in challenging cases and deep learning approaches have proven to be more robust and generalizable.  ...  We generate a large dataset of H&E training images with perfect nuclei segmentation labels using an unpaired GAN framework.  ...  Deep Learning-based Nuclei Segmentation Nuclei segmentation in histopathology images has been extensively studied using a variety of deep learning methods.  ... 
arXiv:1810.00236v2 fatcat:77hv4dlntjhnnheyhzaoospara

Deep Learning Models for Digital Pathology [article]

Aïcha BenTaieb, Ghassan Hamarneh
2019 arXiv   pre-print
In this survey, we summarize the different challenges facing computational systems for digital pathology and provide a review of state-of-the-art works that developed deep learning-based solutions for  ...  Histopathology images; microscopy images of stained tissue biopsies contain fundamental prognostic information that forms the foundation of pathological analysis and diagnostic medicine.  ...  Convolutional Neural Networks In digital pathology, CNNs are the most commonly used type of feed forward neural networks.  ... 
arXiv:1910.12329v2 fatcat:2b7h7i2zwbautewneabghm3bzi

Fast ScanNet: Fast and Dense Analysis of Multi-Gigapixel Whole-Slide Images for Cancer Metastasis Detection

Huangjing Lin, Hao Chen, Simon Graham, Qi Dou, Nasir Rajpoot, Pheng-Ann Heng
2019 IEEE Transactions on Medical Imaging  
In recent years, with the dramatic advance of high-throughput scanning and deep learning technology, automatic analysis of histology from whole-slide images has received a wealth of interest in the field  ...  practice but also densely scans the whole-slide image to achieve accurate predictions on both micro- and macro-metastases.  ...  Chen et al. exploited a deep contour-aware network for gland and nuclei instance segmentation from histopathological images, which significantly outperformed other methods in two recent challenges [36  ... 
doi:10.1109/tmi.2019.2891305 pmid:30624213 fatcat:bdort34gybbjjhsa2t3id5ck4u

Segmentation and Classification in Digital Pathology for Glioma Research: Challenges and Deep Learning Approaches

Tahsin Kurc, Spyridon Bakas, Xuhua Ren, Aditya Bagari, Alexandre Momeni, Yue Huang, Lichi Zhang, Ashish Kumar, Marc Thibault, Qi Qi, Qian Wang, Avinash Kori (+12 others)
2020 Frontiers in Neuroscience  
One method Is a segmentation method designed to segment nuclei in whole slide tissue images (WSIs) of adult diffuse glioma cases.  ...  Accurate extraction and classification of both visual and latent image features Is an increasingly complex challenge due to the increased complexity and resolution of biomedical image data.  ...  Hou et al. (2019b) proposed a sparse convolutional autoencoder for the detection of nuclei and feature extraction in WSIs.  ... 
doi:10.3389/fnins.2020.00027 pmid:32153349 pmcid:PMC7046596 fatcat:he676xn53ff5xbxthrwhun6t4i

A review of deep learning in medical imaging: Image traits, technology trends, case studies with progress highlights, and future promises [article]

S. Kevin Zhou, Hayit Greenspan, Christos Davatzikos, James S. Duncan, Bram van Ginneken, Anant Madabhushi, Jerry L. Prince, Daniel Rueckert, Ronald M. Summers
2020 arXiv   pre-print
We cover the topics of network architecture, sparse and noisy labels, federating learning, interpretability, uncertainty quantification, etc.  ...  Then, we present several case studies that are commonly found in clinical practice, including digital pathology and chest, brain, cardiovascular, and abdominal imaging.  ...  One of the early applications of DL to whole slide pathology images was in the detection and segmentation of individual nuclei. Xu et al.  ... 
arXiv:2008.09104v1 fatcat:z2gic7or4vgnnfcf4joimjha7i

Deep neural network models for computational histopathology: A survey [article]

Chetan L. Srinidhi, Ozan Ciga, Anne L. Martel
2019 arXiv   pre-print
In this paper, we present a comprehensive review of state-of-the-art deep learning approaches that have been used in the context of histopathological image analysis.  ...  Recently, deep learning has become the mainstream methodological choice for analyzing and interpreting cancer histology images.  ...  Automated grading of gliomas using deep learning in digital pathology images: A modular approach with ensemble of convolutional neural networks, in: AMIA Annual Symposium Proceedings, p. 1899.  ... 
arXiv:1912.12378v1 fatcat:xdfkzzwzb5alhjfhffqpcurb2u

Conditional Generative Adversarial Networks for Metal Artifact Reduction in CT Images of the Ear [chapter]

Jianing Wang, Yiyuan Zhao, Jack H. Noble, Benoit M. Dawant
2018 Lecture Notes in Computer Science  
Microscopy Image Super Resolution Liang Han; Zhaozheng Yin* M-146 Multi-Context Deep Network for Angle-Closure Glaucoma Screening in Anterior Segment OCT Analysis of Morphological Changes of the Lamina  ...  Brain Networks During Task and Rest Using BOLD-fMRI Michael Hutel*; Andrew Melbourne; Sebastien Ourselin T-103 Identifying Brain Networks of Multiple Time Scales via Deep Recurrent Neural Network Yan  ...  T-129 Learning Myelin Content in Multiple Sclerosis from Multimodal MRI through Adversarial Training Wen  ... 
doi:10.1007/978-3-030-00928-1_1 fatcat:ypoj3zplm5awljf6u5c2spgiea

Medical Image Segmentation with Limited Supervision: A Review of Deep Network Models

Jialin Peng, Ye Wang
2021 IEEE Access  
application of deep learning models in medical image segmentation.  ...  INDEX TERMS Medical image segmentation, semi-supervised segmentation, partially-supervised segmentation, noisy label, sparse annotation. 36828  ...  [114] combined 3D SSMs with 2D and 3D deep convolutional networks to obtain a robust and accurate segmentation of even highly pathological knee bone and cartilage.  ... 
doi:10.1109/access.2021.3062380 fatcat:r5vsec2yfzcy5nk7wusiftyayu

Deep Learning in Medical Image Analysis

Dinggang Shen, Guorong Wu, Heung-Il Suk
2017 Annual Review of Biomedical Engineering  
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.  ...  In this article, we introduce the fundamentals of deep learning methods; review their successes to image registration, anatomical/cell structures detection, tissue segmentation, computer-aided disease  ...  In this article, we first explain the computational theories of neural networks and deep models (e.g., stacked auto-encoder, deep belief network, deep Boltzmann machine, convolutional neural network) and  ... 
doi:10.1146/annurev-bioeng-071516-044442 pmid:28301734 pmcid:PMC5479722 fatcat:amn6qgpt6fedzp3zejgi4aw66u

Classification of Malaria-Infected Cells Using Deep Convolutional Neural Networks [chapter]

W. David Pan, Yuhang Dong, Dongsheng Wu
2018 Machine Learning - Advanced Techniques and Emerging Applications  
, in light of the overfitting problem associated with training deep convolutional neural networks.  ...  We present some of our recent progresses on highly accurate classification of malaria-infected cells using deep convolutional neural networks.  ...  In this chapter, we present some of our recent progresses on highly accurate classification of malaria-infected cells using deep convolutional neural networks.  ... 
doi:10.5772/intechopen.72426 fatcat:adrobinr7jd4pfq5ifrmpsmhf4

Applications of Deep Learning to Neuro-Imaging Techniques

Guangming Zhu, Bin Jiang, Liz Tong, Yuan Xie, Greg Zaharchuk, Max Wintermark
2019 Frontiers in Neurology  
Many clinical applications based on deep learning and pertaining to radiology have been proposed and studied in radiology for classification, risk assessment, segmentation tasks, diagnosis, prognosis,  ...  There are many other innovative applications of AI in various technical aspects of medical imaging, particularly applied to the acquisition of images, ranging from removing image artifacts, normalizing  ...  Convolutional neural networks (CNN) and recurrent neural networks (RNN) are different types of deep learning methods using artificial neural networks (ANN).  ... 
doi:10.3389/fneur.2019.00869 pmid:31474928 pmcid:PMC6702308 fatcat:yki64mb57jhafduasd3hohfkgi

Super-resolution recurrent convolutional neural networks for learning with multi-resolution whole slide images

Lopamudra Mukherjee, Huu Dat Bui, Adib Keikhosravi, Agnes Loeffler, Kevin W. Eliceiri
2019 Journal of Biomedical Optics  
We study a problem scenario of super-resolution (SR) algorithms in the context of whole slide imaging (WSI), a popular imaging modality in digital pathology.  ...  We propose a recurrent convolutional neural network model, to generate SR images from such multi-resolution WSI datasets.  ...  Acknowledgments This research was supported by UW Laboratory for Optical and Computational Instrumentation, the Morgridge Institute for Research, UW Carbone Cancer Center, NIH R01CA199996, and Semiconductor  ... 
doi:10.1117/1.jbo.24.12.126003 pmid:31837128 pmcid:PMC6910074 fatcat:55fo7jl6lbeczmt5cmpfmq2dfi
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