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SYMPOSIUM - ORIGINAL ARTICLE

Classification of mitotic figures with convolutional neural networks and seeded blob features

Malon Christopher D, Cosatto Eric

Year : 2013| Volume: 4| Issue : 1 | Page no: 9-9

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[Pubmed]  [Google Scholar] [DOI]
2 Novel architecture with selected feature vector for effective classification of mitotic and non-mitotic cells in breast cancer histology images
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Biomedical Signal Processing and Control. 2022; 71: 103212
[Pubmed]  [Google Scholar] [DOI]
3 Mitosis detection techniques in H&E stained breast cancer pathological images: A comprehensive review
Xipeng Pan, Yinghua Lu, Rushi Lan, Zhenbing Liu, Zujun Qin, Huadeng Wang, Zaiyi Liu
Computers & Electrical Engineering. 2021; 91: 107038
[Pubmed]  [Google Scholar] [DOI]
4 Online health status monitoring of high voltage insulators using deep learning model
Dipu Sarkar, Sravan Kumar Gunturi
The Visual Computer. 2021;
[Pubmed]  [Google Scholar] [DOI]
5 BrC-MCDLM: breast Cancer detection using Multi-Channel deep learning model
Jitendra V. Tembhurne, Anupama Hazarika, Tausif Diwan
Multimedia Tools and Applications. 2021; 80(21-23): 31647
[Pubmed]  [Google Scholar] [DOI]
6 Artificial intelligence and digital pathology: Opportunities and implications for immuno-oncology
Faranak Sobhani, Ruth Robinson, Azam Hamidinekoo, Ioannis Roxanis, Navita Somaiah, Yinyin Yuan
Biochimica et Biophysica Acta (BBA) - Reviews on Cancer. 2021; 1875(2): 188520
[Pubmed]  [Google Scholar] [DOI]
7 Computational methods for automated mitosis detection in histopathology images: A review
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Biocybernetics and Biomedical Engineering. 2021; 41(1): 64
[Pubmed]  [Google Scholar] [DOI]
8 DHS-CapsNet : Dual horizontal squash capsule networks for lung and colon cancer classification from whole slide histopathological images
Kwabena Adu, Yongbin Yu, Jingye Cai, Kwabena Owusu-Agyemang, Baidenger Agyekum Twumasi, Xiangxiang Wang
International Journal of Imaging Systems and Technology. 2021; 31(4): 2075
[Pubmed]  [Google Scholar] [DOI]
9 Research on data classification and feature fusion method of cancer nuclei image based on deep learning
Shanshan Liu, Ruo Hu, Jianfang Wu, Xizheng Zhang, Jun He, Huimin Zhao, Huajia Wang, Xiangjun Li
International Journal of Imaging Systems and Technology. 2021;
[Pubmed]  [Google Scholar] [DOI]
10 A new deep convolutional neural network model for classifying breast cancer histopathological images and the hyperparameter optimisation of the proposed model
Kadir Can Burçak, Ömer Kaan Baykan, Harun Uguz
The Journal of Supercomputing. 2021; 77(1): 973
[Pubmed]  [Google Scholar] [DOI]
11 A Comprehensive Analysis of Weakly-Supervised Semantic Segmentation in Different Image Domains
Lyndon Chan, Mahdi S. Hosseini, Konstantinos N. Plataniotis
International Journal of Computer Vision. 2021; 129(2): 361
[Pubmed]  [Google Scholar] [DOI]
12 A deep learning based multiscale approach to segment the areas of interest in whole slide images
Yanbo Feng, Adel Hafiane, Hélène Laurent
Computerized Medical Imaging and Graphics. 2021; 90: 101923
[Pubmed]  [Google Scholar] [DOI]
13 Deep computational pathology in breast cancer
Andrea Duggento, Allegra Conti, Alessandro Mauriello, Maria Guerrisi, Nicola Toschi
Seminars in Cancer Biology. 2021; 72: 226
[Pubmed]  [Google Scholar] [DOI]
14 Multi-magnification-based machine learning as an ancillary tool for the pathologic assessment of shaved margins for breast carcinoma lumpectomy specimens
Timothy M. D’Alfonso, David Joon Ho, Matthew G. Hanna, Anne Grabenstetter, Dig Vijay Kumar Yarlagadda, Luke Geneslaw, Peter Ntiamoah, Thomas J. Fuchs, Lee K. Tan
Modern Pathology. 2021; 34(8): 1487
[Pubmed]  [Google Scholar] [DOI]
15 System for quantitative evaluation of DAB&H-stained breast cancer biopsy digital images (CHISEL)
Lukasz Roszkowiak, Anna Korzynska, Krzysztof Siemion, Jakub Zak, Dorota Pijanowska, Ramon Bosch, Marylene Lejeune, Carlos Lopez
Scientific Reports. 2021; 11(1)
[Pubmed]  [Google Scholar] [DOI]
16 An Open Source Platform for Computational Histopathology
Xiaxia Yu, Bingshuai Zhao, Haofan Huang, Mu Tian, Sai Zhang, Hongping Song, Zengshan Li, Kun Huang, Yi Gao
IEEE Access. 2021; 9: 73651
[Pubmed]  [Google Scholar] [DOI]
17 Attention-Guided Multi-Branch Convolutional Neural Network for Mitosis Detection From Histopathological Images
Haijun Lei, Shaomin Liu, Ahmed Elazab, Xuehao Gong, Baiying Lei
IEEE Journal of Biomedical and Health Informatics. 2021; 25(2): 358
[Pubmed]  [Google Scholar] [DOI]
18 Deep Learning Methods for Lung Cancer Segmentation in Whole-Slide Histopathology Images—The [email protected] Challenge 2019
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IEEE Journal of Biomedical and Health Informatics. 2021; 25(2): 429
[Pubmed]  [Google Scholar] [DOI]
19 DetexNet: Accurately Diagnosing Frequent and Challenging Pediatric Malignant Tumors
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IEEE Transactions on Medical Imaging. 2021; 40(1): 395
[Pubmed]  [Google Scholar] [DOI]
20 Representation of Differential Learning Method for Mitosis Detection
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Journal of Healthcare Engineering. 2021; 2021: 1
[Pubmed]  [Google Scholar] [DOI]
21 AxonDeep: Automated Optic Nerve Axon Segmentation in Mice With Deep Learning
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Translational Vision Science & Technology. 2021; 10(14): 22
[Pubmed]  [Google Scholar] [DOI]
22 Automated knowledge-assisted mitosis cells detection framework in breast histopathology images
Xiao Jian Tan, Nazahah Mustafa, Mohd Yusoff Mashor, Khairul Shakir Ab Rahman
Mathematical Biosciences and Engineering. 2021; 19(2): 1721
[Pubmed]  [Google Scholar] [DOI]
23 Efficient Classification of White Blood Cell Leukemia with Improved Swarm Optimization of Deep Features
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Scientific Reports. 2020; 10(1)
[Pubmed]  [Google Scholar] [DOI]
24 Staged Detection–Identification Framework for Cell Nuclei in Histopathology Images
Xiang Li, Wei Li, Ran Tao
IEEE Transactions on Instrumentation and Measurement. 2020; 69(1): 183
[Pubmed]  [Google Scholar] [DOI]
25 Objective Diagnosis for Histopathological Images Based on Machine Learning Techniques: Classical Approaches and New Trends
Naira Elazab, Hassan Soliman, Shaker El-Sappagh, S. M. Riazul Islam, Mohammed Elmogy
Mathematics. 2020; 8(11): 1863
[Pubmed]  [Google Scholar] [DOI]
26 Hyperspectral Imaging for the Detection of Glioblastoma Tumor Cells in H&E Slides Using Convolutional Neural Networks
Samuel Ortega, Martin Halicek, Himar Fabelo, Rafael Camacho, María de la Luz Plaza, Fred Godtliebsen, Gustavo M. Callicó, Baowei Fei
Sensors. 2020; 20(7): 1911
[Pubmed]  [Google Scholar] [DOI]
27 Hyperspectral and multispectral imaging in digital and computational pathology: a systematic review [Invited]
Samuel Ortega, Martin Halicek, Himar Fabelo, Gustavo M. Callico, Baowei Fei
Biomedical Optics Express. 2020; 11(6): 3195
[Pubmed]  [Google Scholar] [DOI]
28 Artificial Intelligence-Based Multiclass Classification of Benign or Malignant Mucosal Lesions of the Stomach
Bowei Ma, Yucheng Guo, Weian Hu, Fei Yuan, Zhenggang Zhu, Yingyan Yu, Hao Zou
Frontiers in Pharmacology. 2020; 11
[Pubmed]  [Google Scholar] [DOI]
29 Hyperspectral Superpixel-Wise Glioblastoma Tumor Detection in Histological Samples
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Applied Sciences. 2020; 10(13): 4448
[Pubmed]  [Google Scholar] [DOI]
30 Artificial Intelligence-Based Mitosis Detection in Breast Cancer Histopathology Images Using Faster R-CNN and Deep CNNs
Tahir Mahmood, Muhammad Arsalan, Muhammad Owais, Min Beom Lee, Kang Ryoung Park
Journal of Clinical Medicine. 2020; 9(3): 749
[Pubmed]  [Google Scholar] [DOI]
31 PartMitosis: A Partially Supervised Deep Learning Framework for Mitosis Detection in Breast Cancer Histopathology Images
Meriem Sebai, Tianjiang Wang, Saad Ali Al-Fadhli
IEEE Access. 2020; 8: 45133
[Pubmed]  [Google Scholar] [DOI]
32 MitosisNet: End-to-End Mitotic Cell Detection by Multi-Task Learning
Md Zahangir Alom, Theus Aspiras, Tarek M. Taha, T.J. Bowen, Vijayan K. Asari
IEEE Access. 2020; 8: 68695
[Pubmed]  [Google Scholar] [DOI]
33 A Comprehensive Review for Breast Histopathology Image Analysis Using Classical and Deep Neural Networks
Xiaomin Zhou, Chen Li, Md Mamunur Rahaman, Yudong Yao, Shiliang Ai, Changhao Sun, Qian Wang, Yong Zhang, Mo Li, Xiaoyan Li, Tao Jiang, Dan Xue, Shouliang Qi, Yueyang Teng
IEEE Access. 2020; 8: 90931
[Pubmed]  [Google Scholar] [DOI]
34 A machine learning algorithm for simulating immunohistochemistry: development of SOX10 virtual IHC and evaluation on primarily melanocytic neoplasms
Christopher R. Jackson, Aravindhan Sriharan, Louis J. Vaickus
Modern Pathology. 2020; 33(9): 1638
[Pubmed]  [Google Scholar] [DOI]
35 Mitosis detection in breast cancer histopathology images using hybrid feature space
Noorulain Maroof, Asifullah Khan, Shahzad Ahmad Qureshi, Aziz ul Rehman, Rafiullah Khan Khalil, Seong-O Shim
Photodiagnosis and Photodynamic Therapy. 2020; 31: 101885
[Pubmed]  [Google Scholar] [DOI]
36 MaskMitosis: a deep learning framework for fully supervised, weakly supervised, and unsupervised mitosis detection in histopathology images
Meriem Sebai, Xinggang Wang, Tianjiang Wang
Medical & Biological Engineering & Computing. 2020; 58(7): 1603
[Pubmed]  [Google Scholar] [DOI]
37 A comparative study of breast cancer tumor classification by classical machine learning methods and deep learning method
Yadavendra, Satish Chand
Machine Vision and Applications. 2020; 31(6)
[Pubmed]  [Google Scholar] [DOI]
38 Artificial intelligence for microscopy: what you should know
Lucas von Chamier, Romain F. Laine, Ricardo Henriques
Biochemical Society Transactions. 2019; 47(4): 1029
[Pubmed]  [Google Scholar] [DOI]
39 Unsupervised Learning for Cell-Level Visual Representation in Histopathology Images With Generative Adversarial Networks
Bo Hu, Ye Tang, Eric I-Chao Chang, Yubo Fan, Maode Lai, Yan Xu
IEEE Journal of Biomedical and Health Informatics. 2019; 23(3): 1316
[Pubmed]  [Google Scholar] [DOI]
40 Deep learning techniques for detecting preneoplastic and neoplastic lesions in human colorectal histological images
Paola Sena, Rita Fioresi, Francesco Faglioni, Lorena Losi, Giovanni Faglioni, Luca Roncucci
Oncology Letters. 2019;
[Pubmed]  [Google Scholar] [DOI]
41 Glomerulus Classification and Detection Based on Convolutional Neural Networks
Jaime Gallego,Anibal Pedraza,Samuel Lopez,Georg Steiner,Lucia Gonzalez,Arvydas Laurinavicius,Gloria Bueno
Journal of Imaging. 2018; 4(1): 20
[Pubmed]  [Google Scholar] [DOI]
42 AxonDeepSeg: automatic axon and myelin segmentation from microscopy data using convolutional neural networks
Aldo Zaimi,Maxime Wabartha,Victor Herman,Pierre-Louis Antonsanti,Christian S. Perone,Julien Cohen-Adad
Scientific Reports. 2018; 8(1)
[Pubmed]  [Google Scholar] [DOI]
43 DeepMitosis: Mitosis Detection via Deep Detection, Verication and Segmentation Networks
Chao Li,Xinggang Wang,Wenyu Liu,Longin Jan Latecki
Medical Image Analysis. 2018;
[Pubmed]  [Google Scholar] [DOI]
44 Automated Mitosis Detection in Histopathology Based on Non-Gaussian Modeling of Complex Wavelet Coefficients
Tao Wan,Wanshu Zhang,Min Zhu,Jianhui Chen,Alin Achim,Zengchang Qin
Neurocomputing. 2017;
[Pubmed]  [Google Scholar] [DOI]
45 Integrating Segmentation with Deep Learning for Enhanced Classification of Epithelial and Stromal Tissues in H&E Images
Zahraa Al-Milaji,Ilker Ersoy,Adel Hafiane,Kannappan Palaniappan,Filiz Bunyak
Pattern Recognition Letters. 2017;
[Pubmed]  [Google Scholar] [DOI]
46 Deep learning for automated skeletal bone age assessment in X-ray images
C. Spampinato,S. Palazzo,D. Giordano,M. Aldinucci,R. Leonardi
Medical Image Analysis. 2017; 36: 41
[Pubmed]  [Google Scholar] [DOI]
47 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
Medical Image Analysis. 2017; 42: 60
[Pubmed]  [Google Scholar] [DOI]
48 Efficient Deep Learning Model for Mitosis Detection using Breast Histopathology Images
Monjoy Saha,Chandan Chakraborty,Daniel Racoceanu
Computerized Medical Imaging and Graphics. 2017;
[Pubmed]  [Google Scholar] [DOI]
49 Digital image analysis in breast pathology –from image processing techniques to artificial intelligence
Stephanie Robertson,Hossein Azizpour,Kevin Smith,Johan Hartman
Translational Research. 2017;
[Pubmed]  [Google Scholar] [DOI]
50 Automated Classification of Benign and Malignant Proliferative Breast Lesions
Evani Radiya-Dixit,David Zhu,Andrew H. Beck
Scientific Reports. 2017; 7(1)
[Pubmed]  [Google Scholar] [DOI]
51 Deep convolutional neural networks for automatic segmentation of left ventricle cavity from cardiac magnetic resonance images
Xulei Yang,Zeng Zeng,Su Yi
IET Computer Vision. 2017;
[Pubmed]  [Google Scholar] [DOI]
52 Accurate and reproducible invasive breast cancer detection in whole-slide images: A Deep Learning approach for quantifying tumor extent
Angel Cruz-Roa,Hannah Gilmore,Ajay Basavanhally,Michael Feldman,Shridar Ganesan,Natalie N.C. Shih,John Tomaszewski,Fabio A. González,Anant Madabhushi
Scientific Reports. 2017; 7: 46450
[Pubmed]  [Google Scholar] [DOI]
53 Computational approach for mitotic cell detection and its application in oral squamous cell carcinoma
Dev Kumar Das,Pabitra Mitra,Chandan Chakraborty,Sanjoy Chatterjee,Asok Kumar Maiti,Surajit Bose
Multidimensional Systems and Signal Processing. 2017;
[Pubmed]  [Google Scholar] [DOI]
54 Two-phase deep convolutional neural network for reducing class skewness in histopathological images based breast cancer detection
Noorul Wahab,Asifullah Khan,Yeon Soo Lee
Computers in Biology and Medicine. 2017; 85: 86
[Pubmed]  [Google Scholar] [DOI]
55 Conceptual data sampling for breast cancer histology image classification
Eman Rezk,Zainab Awan,Fahad Islam,Ali Jaoua,Somaya Al Maadeed,Nan Zhang,Gautam Das,Nasir Rajpoot
Computers in Biology and Medicine. 2017; 89: 59
[Pubmed]  [Google Scholar] [DOI]
56 A Multi-Classifier System for Automatic Mitosis Detection in Breast Histopathology Images Using Deep Belief Networks
K. Sabeena Beevi,Madhu S. Nair,G. R. Bindu
IEEE Journal of Translational Engineering in Health and Medicine. 2017; 5: 1
[Pubmed]  [Google Scholar] [DOI]
57 Introduction of Artificial Intelligence in Pathology
SangYong Song
Hanyang Medical Reviews. 2017; 37(2): 77
[Pubmed]  [Google Scholar] [DOI]
58 Segmentation and classification of colon glands with deep convolutional neural networks and total variation regularization
Philipp Kainz,Michael Pfeiffer,Martin Urschler
PeerJ. 2017; 5: e3874
[Pubmed]  [Google Scholar] [DOI]
59 Locality Sensitive Deep Learning for Detection and Classification of Nuclei in Routine Colon Cancer Histology Images
Korsuk Sirinukunwattana,Shan E Ahmed Raza,Yee-Wah Tsang,David R. J. Snead,Ian A. Cree,Nasir M. Rajpoot
IEEE Transactions on Medical Imaging. 2016; 35(5): 1196
[Pubmed]  [Google Scholar] [DOI]
60 Using Automated Image Analysis Algorithms to Distinguish Normal, Aberrant, and Degenerate Mitotic Figures Induced by Eg5 Inhibition
Alison L. Bigley,Stephanie K. Klein,Barry Davies,Leigh Williams,Daniel G. Rudmann
Toxicologic Pathology. 2016; 44(5): 663
[Pubmed]  [Google Scholar] [DOI]
61 Automatic detection of breast cancer mitotic cells based on the combination of textural, statistical and innovative mathematical features
Ashkan Tashk,Mohammad Sadegh Helfroush,Habibollah Danyali,Mojgan Akbarzadeh-jahromi
Applied Mathematical Modelling. 2015; 39(20): 6165
[Pubmed]  [Google Scholar] [DOI]
62 An unsupervised feature learning framework for basal cell carcinoma image analysis
John Arevalo,Angel Cruz-Roa,Viviana Arias,Eduardo Romero,Fabio A. González
Artificial Intelligence in Medicine. 2015; 64(2): 131
[Pubmed]  [Google Scholar] [DOI]
63 Multispectral Band Selection and Spatial Characterization: Application to Mitosis Detection in Breast Cancer Histopathology
H. Irshad,A. Gouaillard,L. Roux,D. Racoceanu
Computerized Medical Imaging and Graphics. 2014;
[Pubmed]  [Google Scholar] [DOI]
64 Methods for Nuclei Detection, Segmentation, and Classification in Digital Histopathology: A Review—Current Status and Future Potential
Humayun Irshad,Antoine Veillard,Ludovic Roux,Daniel Racoceanu
IEEE Reviews in Biomedical Engineering. 2014; 7: 97
[Pubmed]  [Google Scholar] [DOI]
65 Breast Cancer Histopathology Image Analysis: A Review
Mitko Veta,Josien P. W. Pluim,Paul J. van Diest,Max A. Viergever
IEEE Transactions on Biomedical Engineering. 2014; 61(5): 1400
[Pubmed]  [Google Scholar] [DOI]

 

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