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A Tetrahedron-Based Heat Flux Signature for Cortical Thickness Morphometry Analysis
[chapter]
2018
Lecture Notes in Computer Science
Preserved Fulltext
314 Densely Deep Supervised Networks with Threshold Loss for Cancer Detection in Automated Breast Ultrasound 316 SPNet: Shape Prediction using a Fully Convolutional Neural Network 317 Modeling Longitudinal ...
and Fully Convolutional Neural Network 746 Exploiting Partial Structural Symmetry For Patient-Specific Image Augmentation in Trauma Interventions 753 Multimodal Recurrent Model with Attention for Automated ...
doi:10.1007/978-3-030-00931-1_48
pmid:30338317
pmcid:PMC6191198
fatcat:dqhvpm5xzrdqhglrfftig3qejq
Front Matter: Volume 11313
2020
Medical Imaging 2020: Image Processing
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Utilization of CIDs allows articles to be fully citable as soon as they are published online, and connects the same identifier to all online and print versions of the publication. ...
The papers in this volume were part of the technical conference cited on the cover and title page. Papers were selected and subject to review by the editors and conference program committee. ...
of multi-task learning for lung cancer detection [11313-74]
11313 24
Reduction of motion artifacts in head CT exams using multi-scale convolutional neural network
[11313-75]
11313 25
CAI-UNet for ...
doi:10.1117/12.2570657
fatcat:be32besqknaybh6wibz7unuboa
Front Matter: Volume 10140
2017
Medical Imaging 2017: Digital Pathology
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Utilization of CIDs allows articles to be fully citable as soon as they are published online, and connects the same identifier to all online and print versions of the publication. ...
. The last two digits indicate publication order within the volume using a Base 36 numbering system employing both numerals and letters. These two-number sets start with 00, 01, 02, 03, 04, ...
images through large scale image
synthesis [10140-19]
POSTER SESSION
10140 0M
10140 0O
Convolutional neural networks for an automatic classification of prostate tissue slides with
high-grade Gleason ...
doi:10.1117/12.2270372
dblp:conf/midp/X17
fatcat:6yeb63ix6bau7jhipthayjih24
A review of machine learning approaches, challenges and prospects for computational tumor pathology
[article]
2022
arXiv
pre-print
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better-integrated solutions for whole-slide images, multi-omics data, and clinical informatics. ...
This review investigates image preprocessing methods in computational pathology from a pathological and technical perspective, machine learning-based methods, and applications of computational pathology ...
Convolutional neural networks reduce the complex pipeline of histopathology images, and FCN-8, two SegNet variants, and multi-scale U-Net showed high accuracy in segmenting high-and low-grade tumors, respectively ...
arXiv:2206.01728v1
fatcat:g7r7fsw2bzafpkkyg6hpzjyt5e
Point Supervised Extended Scenario Nuclear Analysis Framework Based on LSTM-CFCN
2020
IEEE Access
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In this paper, we seek a different route and propose a novel efficient framework for robust cell analysis based on Long Short Term Memory Channeled Fully Convolution Neural Networks (LSTM-CFCN). ...
INDEX TERMS Cell analysis, LSTM-CFCN, multi-task learning, multi-scale image processing. ...
TRAINING DATA PREPARATION 1) CELL IMAGE PREPOSSESSING Biomedical images collected from microscopy are with multi-scale and large size, such as the pathological images
FIGURE 3 . 3 Mask generation using ...
doi:10.1109/access.2020.2984996
fatcat:ovzdwaef7jcfli7prfr253sema
Classification and Generation of Microscopy Images with Plasmodium Falciparum via Artificial Neural Networks
[article]
2020
bioRxiv
pre-print
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Moreover, generative adversial networks offer new opportunities in microscopy: data homogenization, and increase of images in case of imbalanced or small sample size. ...
Recent studies use machine-learning techniques to detect parasites in microscopy images automatically. However, these tools are trained and tested in specific datasets. ...
Acknowledgements We would like to thank Ekene Kwabena Nwaefuna of Ghana Atomic Energy Commission for participaitng to the validation test between real and synthetic images. ...
doi:10.1101/2020.07.21.214742
fatcat:nvylk2msuvbgbhj72bai6p2x6q
Front Matter: Volume 12032
2022
Medical Imaging 2022: Image Processing
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Utilization of CIDs allows articles to be fully citable as soon as they are published online, and connects the same identifier to all online and print versions of the publication. ...
. The last two digits indicate publication order within the volume using a Base 36 numbering system employing both numerals and letters. These two-number sets start with 00, ...
segmentation from laparoscopic videos [12032-32] 0V Convolutional neural network with a hybrid loss function for fully automated segmentation of lymphoma lesions in FDG PET images [12032-33] 0W iv Proc ...
doi:10.1117/12.2638192
fatcat:ikfgnjefaba2tpiamxoftyi6sa
Computational biology: deep learning
2017
Emerging Topics in Life Sciences
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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. ...
This exciting class of methods, based on artificial neural networks, quickly became popular due to its competitive performance in prediction problems. ...
Convolutional neural networks could accurately detect phototoxicity [62] and cell-cycle states [63] from images. ...
doi:10.1042/etls20160025
pmid:33525807
pmcid:PMC7289034
fatcat:qnw2yndsp5aqlnxxshtaipzctu
Towards pixel-to-pixel deep nucleus detection in microscopy images
2019
BMC Bioinformatics
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Deep neural networks are emerging as a powerful tool for biomedical image computing; in particular, convolutional neural networks have been widely applied to nucleus/cell detection in microscopy images ...
More specifically, we present a fully convolutional network-based regression model and extensively evaluate it on large-scale digital pathology and microscopy image datasets, which consist of 23 organs ...
Acknowledgements We thank the TCGA Research Network for data access. We also thank all the BICI2 lab members for their support and discussion. ...
doi:10.1186/s12859-019-3037-5
pmid:31521104
pmcid:PMC6744696
fatcat:3fl4j5vfv5fg3j464flamuxdh4
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
Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. ...
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. ...
Papers not reporting results on medical image data or only using standard feed-forward neural networks with handcrafted features were excluded. ...
doi:10.1016/j.media.2017.07.005
pmid:28778026
fatcat:esbj72ftwvbgzh6jgw367k73j4
Deep Learning‐Based Single‐Cell Optical Image Studies
2020
Cytometry Part A
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In recent years, deep learning has been prevalent in the field of machine learning for large-scale image processing and analysis, which brings a new dawn for single-cell optical image studies with an explosive ...
In addition, deep learning in popular single-cell optical imaging techniques such as label-free cell optical imaging, high-content screening, and high-throughput optical imaging cytometry are also mentioned ...
For example, FCN replaces the fully connected layers of CNN with the convolution layers, allowing the entire network to be concatenated by convolution. ...
doi:10.1002/cyto.a.23973
pmid:31981309
fatcat:4a4ip4kaf5hudokpfqub3a273y
Deep Learning Models for Digital Pathology
[article]
2019
arXiv
pre-print
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Histopathology images; microscopy images of stained tissue biopsies contain fundamental prognostic information that forms the foundation of pathological analysis and diagnostic medicine. ...
However digitized histopathology tissue slides are unique in a variety of ways and come with their own set of computational challenges. ...
Convolutional Neural Networks In digital pathology, CNNs are the most commonly used type of feed forward neural networks. ...
arXiv:1910.12329v2
fatcat:2b7h7i2zwbautewneabghm3bzi
Deep learning for magnification independent breast cancer histopathology image classification
2016
2016 23rd International Conference on Pattern Recognition (ICPR)
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In this work, we propose to classify breast cancer histopathology images independent of their magnifications using convolutional neural networks (CNNs). ...
Advances in digital imaging techniques offers assessment of pathology images using computer vision and machine learning methods which could automate some of the tasks in the diagnostic pathology workflow ...
ACKNOWLEDGMENT The authors would like to thank to Fabio Alexandre Spanhol for detailed discussions about BreaKHis database and baseline results. ...
doi:10.1109/icpr.2016.7900002
dblp:conf/icpr/BayramogluKH16
fatcat:vga2jhvbhzeifgzm7s5gpvc4ge
Deep Learning in Image Cytometry: A Review
2018
Cytometry Part A
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In this review, we focus on deep learning and how it is applied to microscopy image data of cells and tissue samples. ...
Artificial intelligence, deep convolutional neural networks, and deep learning are all niche terms that are increasingly appearing in scientific presentations as well as in the general media. ...
ACKNOWLEDGMENTS This project was financially supported by the Swedish Foundation for Strategic Research (grants BD15-0008 and SB16-0046), the European Research Council (grant ERC-2015-CoG 682810), and ...
doi:10.1002/cyto.a.23701
pmid:30565841
pmcid:PMC6590257
fatcat:dszbcsfncrhxnazsxopjkbe3ju
An Efficient Multi-Level Convolutional Neural Network Approach for White Blood Cells Classification
2022
Diagnostics
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Once separated, two parallel convolutional neural networks with the MobileNet structure are used to recognize the subclasses in the second level. ...
This work presents a two-stage hybrid multi-level scheme that efficiently classifies four cell groups: lymphocytes and monocytes (mononuclear) and segmented neutrophils and eosinophils (polymorphonuclear ...
ML-CNN (Our proposal) Multi-level convolutional neural network approach with multi-source datasets. Combines Faster R-CNN for cell detection with a MobileNet for type classification. ...
doi:10.3390/diagnostics12020248
pmid:35204339
pmcid:PMC8871319
fatcat:qoju3ajin5gink4qemxoaosqlu
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