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A Tetrahedron-Based Heat Flux Signature for Cortical Thickness Morphometry Analysis [chapter]

Yonghui Fan, Gang Wang, Natasha Lepore, Yalin Wang
2018 Lecture Notes in Computer Science  
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

Bennett A. Landman, Ivana Išgum
2020 Medical Imaging 2020: Image Processing  
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

Proceedings of SPIE, Metin N. Gurcan, John E. Tomaszewski
2017 Medical Imaging 2017: Digital Pathology  
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]

Liangrui Pan, Zhichao Feng, Shaoliang Peng
2022 arXiv   pre-print
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

Dong Sui, Maozu Guo, Fei Yang, Lei Zhang
2020 IEEE Access  
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]

Rija Tonny Christian Ramarolahy, Esther Opoku Gyasi, Alessandro Crimi
2020 bioRxiv   pre-print
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

Ivana Išgum, Olivier Colliot
2022 Medical Imaging 2022: Image Processing  
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

William Jones, Kaur Alasoo, Dmytro Fishman, Leopold Parts
2017 Emerging Topics in Life Sciences  
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

Fuyong Xing, Yuanpu Xie, Xiaoshuang Shi, Pingjun Chen, Zizhao Zhang, Lin Yang
2019 BMC Bioinformatics  
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

Geert Litjens, Thijs Kooi, Babak Ehteshami Bejnordi, Arnaud Arindra Adiyoso Setio, Francesco Ciompi, Mohsen Ghafoorian, Jeroen A.W.M. van der Laak, Bram van Ginneken, Clara I. Sánchez
2017 Medical Image Analysis  
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

Jing Sun, Attila Tárnok, Xuantao Su
2020 Cytometry Part A  
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]

Aïcha BenTaieb, Ghassan Hamarneh
2019 arXiv   pre-print
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

Neslihan Bayramoglu, Juho Kannala, Janne Heikkila
2016 2016 23rd International Conference on Pattern Recognition (ICPR)  
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

Anindya Gupta, Philip J. Harrison, Håkan Wieslander, Nicolas Pielawski, Kimmo Kartasalo, Gabriele Partel, Leslie Solorzano, Amit Suveer, Anna H. Klemm, Ola Spjuth, Ida‐Maria Sintorn, Carolina Wählby
2018 Cytometry Part A  
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

César Cheuque, Marvin Querales, Roberto León, Rodrigo Salas, Romina Torres
2022 Diagnostics  
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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