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SYMPOSIUM - ORIGINAL ARTICLE

Mitosis detection in breast cancer histological images An ICPR 2012 contest

Roux Ludovic, Racoceanu Daniel, Loménie Nicolas, Kulikova Maria, Irshad Humayun, Klossa Jacques, Capron Frédérique, Genestie Catherine, Naour Gilles Le, Gurcan Metin N

Year : 2013| Volume: 4| Issue : 1 | Page no: 8-8

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1 Deep learning-based automated mitosis detection in histopathology images for breast cancer grading
Tojo Mathew, B. Ajith, Jyoti R. Kini, Jeny Rajan
International Journal of Imaging Systems and Technology. 2022;
[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
Mobeen Ur Rehman, Suhail Akhtar, Muhammad Zakwan, Muhammad Habib Mahmood
Biomedical Signal Processing and Control. 2022; 71: 103212
[Pubmed]  [Google Scholar] [DOI]
3 Self supervised contrastive learning for digital histopathology
Ozan Ciga, Tony Xu, Anne Louise Martel
Machine Learning with Applications. 2022; 7: 100198
[Pubmed]  [Google Scholar] [DOI]
4 Computer-Aided Assessment of Melanocytic Lesions by Means of a Mitosis Algorithm
Bart Sturm, David Creytens, Jan Smits, Ariadne H. A. G. Ooms, Erik Eijken, Eline Kurpershoek, Heidi V. N. Küsters-Vandevelde, Carla Wauters, Willeke A. M. Blokx, Jeroen A. W. M. van der Laak
Diagnostics. 2022; 12(2): 436
[Pubmed]  [Google Scholar] [DOI]
5 Machine Learning Methods for Histopathological Image Analysis: A Review
Jonathan de Matos, Steve Ataky, Alceu de Souza Britto, Luiz Soares de Oliveira, Alessandro Lameiras Koerich
Electronics. 2021; 10(5): 562
[Pubmed]  [Google Scholar] [DOI]
6 Evaluating Cancer-Related Biomarkers Based on Pathological Images: A Systematic Review
Xiaoliang Xie, Xulin Wang, Yuebin Liang, Jingya Yang, Yan Wu, Li Li, Xin Sun, Pingping Bing, Binsheng He, Geng Tian, Xiaoli Shi
Frontiers in Oncology. 2021; 11
[Pubmed]  [Google Scholar] [DOI]
7 A Review of Computer-Aided Expert Systems for Breast Cancer Diagnosis
Xin Yu Liew, Nazia Hameed, Jeremie Clos
Cancers. 2021; 13(11): 2764
[Pubmed]  [Google Scholar] [DOI]
8 An investigation of XGBoost-based algorithm for breast cancer classification
Xin Yu Liew, Nazia Hameed, Jeremie Clos
Machine Learning with Applications. 2021; 6: 100154
[Pubmed]  [Google Scholar] [DOI]
9 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]
10 Deep learning powers cancer diagnosis in digital pathology
Yunjie He, Hong Zhao, Stephen T.C. Wong
Computerized Medical Imaging and Graphics. 2021; 88: 101820
[Pubmed]  [Google Scholar] [DOI]
11 Machine learning techniques for mitoses classification
Shima Nofallah, Sachin Mehta, Ezgi Mercan, Stevan Knezevich, Caitlin J. May, Donald Weaver, Daniela Witten, Joann G. Elmore, Linda Shapiro
Computerized Medical Imaging and Graphics. 2021; 87: 101832
[Pubmed]  [Google Scholar] [DOI]
12 A review on image-based approaches for breast cancer detection, segmentation, and classification
Zahra Rezaei
Expert Systems with Applications. 2021; 182: 115204
[Pubmed]  [Google Scholar] [DOI]
13 An explainable ensemble feedforward method with Gaussian convolutional filter
Jingchen Li, Haobin Shi, Kao-Shing Hwang
Knowledge-Based Systems. 2021; 225: 107103
[Pubmed]  [Google Scholar] [DOI]
14 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]
15 A deep learning approach for mitosis detection: Application in tumor proliferation prediction from whole slide images
Ramin Nateghi, Habibollah Danyali, Mohammad Sadegh Helfroush
Artificial Intelligence in Medicine. 2021; 114: 102048
[Pubmed]  [Google Scholar] [DOI]
16 Breast cancer intelligent analysis of histopathological data: A systematic review
Felipe André Zeiser, Cristiano André da Costa, Adriana Vial Roehe, Rodrigo da Rosa Righi, Nuno Miguel Cavalheiro Marques
Applied Soft Computing. 2021; 113: 107886
[Pubmed]  [Google Scholar] [DOI]
17 Computational methods for automated mitosis detection in histopathology images: A review
Tojo Mathew, Jyoti R. Kini, Jeny Rajan
Biocybernetics and Biomedical Engineering. 2021; 41(1): 64
[Pubmed]  [Google Scholar] [DOI]
18 Artificial intelligence applied to breast pathology
Mustafa Yousif, Paul J. van Diest, Arvydas Laurinavicius, David Rimm, Jeroen van der Laak, Anant Madabhushi, Stuart Schnitt, Liron Pantanowitz
Virchows Archiv. 2021;
[Pubmed]  [Google Scholar] [DOI]
19 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]
20 Searching Images for Consensus
Hamid R. Tizhoosh, Phedias Diamandis, Clinton J.V. Campbell, Amir Safarpoor, Shivam Kalra, Danial Maleki, Abtin Riasatian, Morteza Babaie
The American Journal of Pathology. 2021; 191(10): 1702
[Pubmed]  [Google Scholar] [DOI]
21 Deep Convolutional Neural Network for Computer-Aided Detection of Breast Cancer Using Histopathology Images
R Karthiga, K Narashimhan
Journal of Physics: Conference Series. 2021; 1767(1): 012042
[Pubmed]  [Google Scholar] [DOI]
22 SmallMitosis: Small Size Mitotic Cells Detection in Breast Histopathology Images
Tasleem Kausar, Mingjiang Wang, M. Adnan Ashraf, Adeeba Kausar
IEEE Access. 2021; 9: 905
[Pubmed]  [Google Scholar] [DOI]
23 Contextual Prior Constrained Deep Networks for Mitosis Detection With Point Annotations
Jiangxiao Han, Xinggang Wang, Wenyu Liu
IEEE Access. 2021; 9: 71954
[Pubmed]  [Google Scholar] [DOI]
24 A multi-phase deep CNN based mitosis detection framework for breast cancer histopathological images
Anabia Sohail, Asifullah Khan, Noorul Wahab, Aneela Zameer, Saranjam Khan
Scientific Reports. 2021; 11(1)
[Pubmed]  [Google Scholar] [DOI]
25 CAD systems for colorectal cancer from WSI are still not ready for clinical acceptance
Sara P. Oliveira, Pedro C. Neto, João Fraga, Diana Montezuma, Ana Monteiro, João Monteiro, Liliana Ribeiro, Sofia Gonçalves, Isabel M. Pinto, Jaime S. Cardoso
Scientific Reports. 2021; 11(1)
[Pubmed]  [Google Scholar] [DOI]
26 A Large-Scale Fully Annotated Low-Cost Microscopy Image Dataset for Deep Learning Framework
Sumona Biswas, Shovan Barma
IEEE Transactions on NanoBioscience. 2021; 20(4): 507
[Pubmed]  [Google Scholar] [DOI]
27 Artificial intelligence for solid tumour diagnosis in digital pathology
Christophe Klein, Qinghe Zeng, Floriane Arbaretaz, Estelle Devêvre, Julien Calderaro, Nicolas Lomenie, Maria Chiara Maiuri
British Journal of Pharmacology. 2021; 178(21): 4291
[Pubmed]  [Google Scholar] [DOI]
28 Assessment of mitotic activity in breast cancer: revisited in the digital pathology era
Asmaa Ibrahim, Ayat Lashen, Michael Toss, Raluca Mihai, Emad Rakha
Journal of Clinical Pathology. 2021; : jclinpath-
[Pubmed]  [Google Scholar] [DOI]
29 Different CNN-based Architectures for Detection of Invasive Ductal Carcinoma in Breast Using Histopathology Images
Isha Gupta, Sheifali Gupta, Swati Singh
International Journal of Image and Graphics. 2021; 21(05)
[Pubmed]  [Google Scholar] [DOI]
30 A Two-Phase Mitosis Detection Approach Based on U-Shaped Network
Wenjing Lu, Qiushi Zhao
BioMed Research International. 2021; 2021: 1
[Pubmed]  [Google Scholar] [DOI]
31 Deep Learning in Cancer Diagnosis and Prognosis Prediction: A Minireview on Challenges, Recent Trends, and Future Directions
Ahsan Bin Tufail, Yong-Kui Ma, Mohammed K. A. Kaabar, Francisco Martínez, A. R. Junejo, Inam Ullah, Rahim Khan, Iman Yi Liao
Computational and Mathematical Methods in Medicine. 2021; 2021: 1
[Pubmed]  [Google Scholar] [DOI]
32 Accuracy and efficiency of an artificial intelligence tool when counting breast mitoses
Liron Pantanowitz, Douglas Hartman, Yan Qi, Eun Yoon Cho, Beomseok Suh, Kyunghyun Paeng, Rajiv Dhir, Pamela Michelow, Scott Hazelhurst, Sang Yong Song, Soo Youn Cho
Diagnostic Pathology. 2020; 15(1)
[Pubmed]  [Google Scholar] [DOI]
33 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]
34 Class-Agnostic Weighted Normalization of Staining in Histopathology Images Using a Spatially Constrained Mixture Model
Sobhan Shafiei, Amir Safarpoor, Ahad Jamalizadeh, H. R. Tizhoosh
IEEE Transactions on Medical Imaging. 2020; 39(11): 3355
[Pubmed]  [Google Scholar] [DOI]
35 Review of the current state of digital image analysis in breast pathology
Martin C. Chang, Miralem Mrkonjic
The Breast Journal. 2020; 26(6): 1208
[Pubmed]  [Google Scholar] [DOI]
36 Deep learning algorithms out-perform veterinary pathologists in detecting the mitotically most active tumor region
Marc Aubreville, Christof A. Bertram, Christian Marzahl, Corinne Gurtner, Martina Dettwiler, Anja Schmidt, Florian Bartenschlager, Sophie Merz, Marco Fragoso, Olivia Kershaw, Robert Klopfleisch, Andreas Maier
Scientific Reports. 2020; 10(1)
[Pubmed]  [Google Scholar] [DOI]
37 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]
38 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]
39 A Comparative Evaluation of Texture Features for Semantic Segmentation of Breast Histopathological Images
R. Rashmi, Keerthana Prasad, Chethana Babu K. Udupa, V. Shwetha
IEEE Access. 2020; 8: 64331
[Pubmed]  [Google Scholar] [DOI]
40 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]
41 A large-scale optical microscopy image dataset of potato tuber for deep learning based plant cell assessment
Sumona Biswas, Shovan Barma
Scientific Data. 2020; 7(1)
[Pubmed]  [Google Scholar] [DOI]
42 A completely annotated whole slide image dataset of canine breast cancer to aid human breast cancer research
Marc Aubreville, Christof A. Bertram, Taryn A. Donovan, Christian Marzahl, Andreas Maier, Robert Klopfleisch
Scientific Data. 2020; 7(1)
[Pubmed]  [Google Scholar] [DOI]
43 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]
44 Deep learning in digital pathology image analysis: a survey
Shujian Deng, Xin Zhang, Wen Yan, Eric I-Chao Chang, Yubo Fan, Maode Lai, Yan Xu
Frontiers of Medicine. 2020; 14(4): 470
[Pubmed]  [Google Scholar] [DOI]
45 Computer-Aided Histopathological Image Analysis Techniques for Automated Nuclear Atypia Scoring of Breast Cancer: a Review
Asha Das, Madhu S. Nair, S. David Peter
Journal of Digital Imaging. 2020; 33(5): 1091
[Pubmed]  [Google Scholar] [DOI]
46 A bird’s-eye view of deep learning in bioimage analysis
Erik Meijering
Computational and Structural Biotechnology Journal. 2020; 18: 2312
[Pubmed]  [Google Scholar] [DOI]
47 Predict Ki-67 Positive Cells in H&E-Stained Images Using Deep Learning Independently From IHC-Stained Images
Yiqing Liu, Xi Li, Aiping Zheng, Xihan Zhu, Shuting Liu, Mengying Hu, Qianjiang Luo, Huina Liao, Mubiao Liu, Yonghong He, Yupeng Chen
Frontiers in Molecular Biosciences. 2020; 7
[Pubmed]  [Google Scholar] [DOI]
48 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]
49 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]
50 Value of public challenges for the development of pathology deep learning algorithms
DouglasJoseph Hartman, JeroenA. W. M. Van Der Laak, MetinN Gurcan, Liron Pantanowitz
Journal of Pathology Informatics. 2020; 11(1): 7
[Pubmed]  [Google Scholar] [DOI]
51 Histo-genomics: digital pathology at the forefront of precision medicine
Ivraym Barsoum, Eriny Tawedrous, Hala Faragalla, George M. Yousef
Diagnosis. 2019; 6(3): 203
[Pubmed]  [Google Scholar] [DOI]
52 Whole-Slide Mitosis Detection in H&E Breast Histology Using PHH3 as a Reference to Train Distilled Stain-Invariant Convolutional Networks
David Tellez, Maschenka Balkenhol, Irene Otte-Holler, Rob van de Loo, Rob Vogels, Peter Bult, Carla Wauters, Willem Vreuls, Suzanne Mol, Nico Karssemeijer, Geert Litjens, Jeroen van der Laak, Francesco Ciompi
IEEE Transactions on Medical Imaging. 2018; 37(9): 2126
[Pubmed]  [Google Scholar] [DOI]
53 Deep learning based tissue analysis predicts outcome in colorectal cancer
Dmitrii Bychkov,Nina Linder,Riku Turkki,Stig Nordling,Panu E. Kovanen,Clare Verrill,Margarita Walliander,Mikael Lundin,Caj Haglund,Johan Lundin
Scientific Reports. 2018; 8(1)
[Pubmed]  [Google Scholar] [DOI]
54 Machine Learning Methods for Histopathological Image Analysis
Daisuke Komura,Shumpei Ishikawa
Computational and Structural Biotechnology Journal. 2018; 16: 34
[Pubmed]  [Google Scholar] [DOI]
55 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]
56 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]
57 Gland segmentation in colon histology images: The glas challenge contest
Korsuk Sirinukunwattana,Josien P.W. Pluim,Hao Chen,Xiaojuan Qi,Pheng-Ann Heng,Yun Bo Guo,Li Yang Wang,Bogdan J. Matuszewski,Elia Bruni,Urko Sanchez,Anton Böhm,Olaf Ronneberger,Bassem Ben Cheikh,Daniel Racoceanu,Philipp Kainz,Michael Pfeiffer,Martin Urschler,David R.J. Snead,Nasir M. Rajpoot
Medical Image Analysis. 2017; 35: 489
[Pubmed]  [Google Scholar] [DOI]
58 DCAN: Deep contour-aware networks for object instance segmentation from histology images
Hao Chen,Xiaojuan Qi,Lequan Yu,Qi Dou,Jing Qin,Pheng-Ann Heng
Medical Image Analysis. 2017; 36: 135
[Pubmed]  [Google Scholar] [DOI]
59 A deep learning based strategy for identifying and associating mitotic activity with gene expression derived risk categories in estrogen receptor positive breast cancers
David Romo-Bucheli,Andrew Janowczyk,Hannah Gilmore,Eduardo Romero,Anant Madabhushi
Cytometry Part A. 2017;
[Pubmed]  [Google Scholar] [DOI]
60 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]
61 Deep learning in robotics: a review of recent research
Harry A. Pierson,Michael S. Gashler
Advanced Robotics. 2017; 31(16): 821
[Pubmed]  [Google Scholar] [DOI]
62 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]
63 SlideJ: An ImageJ plugin for automated processing of whole slide images
Vincenzo Della Mea,Giulia L. Baroni,David Pilutti,Carla Di Loreto,Helmut Ahammer
PLOS ONE. 2017; 12(7): e0180540
[Pubmed]  [Google Scholar] [DOI]
64 Introduction of Artificial Intelligence in Pathology
SangYong Song
Hanyang Medical Reviews. 2017; 37(2): 77
[Pubmed]  [Google Scholar] [DOI]
65 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]
66 Adaptive Dimensionality Reduction with Semi-Supervision (AdDReSS): Classifying Multi-Attribute Biomedical Data
George Lee,David Edmundo Romo Bucheli,Anant Madabhushi,Daoqiang Zhang
PLOS ONE. 2016; 11(7): e0159088
[Pubmed]  [Google Scholar] [DOI]
67 Mitosis Counting in Breast Cancer: Object-Level Interobserver Agreement and Comparison to an Automatic Method
Mitko Veta,Paul J. van Diest,Mehdi Jiwa,Shaimaa Al-Janabi,Josien P. W. Pluim,Anna Sapino
PLOS ONE. 2016; 11(8): e0161286
[Pubmed]  [Google Scholar] [DOI]
68 Automated Segmentation of Nuclei in Breast Cancer Histopathology Images
Maqlin Paramanandam,Michael O’Byrne,Bidisha Ghosh,Joy John Mammen,Marie Therese Manipadam,Robinson Thamburaj,Vikram Pakrashi,Pei-Yi Chu
PLOS ONE. 2016; 11(9): e0162053
[Pubmed]  [Google Scholar] [DOI]
69 Image Montaging for Creating a Virtual Pathology Slide: An Innovative and Economical Tool to Obtain a Whole Slide Image
Spoorthi Ravi Banavar,Prashanthi Chippagiri,Rohit Pandurangappa,Saileela Annavajjula,Premalatha Bidadi Rajashekaraiah
Analytical Cellular Pathology. 2016; 2016: 1
[Pubmed]  [Google Scholar] [DOI]
70 Robust Nucleus/Cell Detection and Segmentation in Digital Pathology and Microscopy Images: A Comprehensive Review
Fuyong Xing,Lin Yang
IEEE Reviews in Biomedical Engineering. 2016; 9: 234
[Pubmed]  [Google Scholar] [DOI]
71 Primer for Image Informatics in Personalized Medicine
Young Hwan Chang,Patrick Foley,Vahid Azimi,Rohan Borkar,Jonathan Lefman
Procedia Engineering. 2016; 159: 58
[Pubmed]  [Google Scholar] [DOI]
72 Imagining the future of bioimage analysis
Erik Meijering,Anne E Carpenter,Hanchuan Peng,Fred A Hamprecht,Jean-Christophe Olivo-Marin
Nature Biotechnology. 2016; 34(12): 1250
[Pubmed]  [Google Scholar] [DOI]
73 A survey on automated cancer diagnosis from histopathology images
J. Angel Arul Jothi,V. Mary Anita Rajam
Artificial Intelligence Review. 2016;
[Pubmed]  [Google Scholar] [DOI]
74 Quantitative analysis of nuclear shape in oral squamous cell carcinoma is useful for predicting the chemotherapeutic response
Maki Ogura,Yoichiro Yamamoto,Hitoshi Miyashita,Hiroyuki Kumamoto,Manabu Fukumoto
Medical Molecular Morphology. 2015;
[Pubmed]  [Google Scholar] [DOI]
75 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]
76 Mapping spatial heterogeneity in the tumor microenvironment: a new era for digital pathology
Andreas Heindl,Sidra Nawaz,Yinyin Yuan
Laboratory Investigation. 2015; 95(4): 377
[Pubmed]  [Google Scholar] [DOI]
77 Beyond immune density: critical role of spatial heterogeneity in estrogen receptor-negative breast cancer
Sidra Nawaz,Andreas Heindl,Konrad Koelble,Yinyin Yuan
Modern Pathology. 2015; 28(6): 766
[Pubmed]  [Google Scholar] [DOI]
78 Automated Histology Analysis: Opportunities for signal processing
Michael T McCann,John A. Ozolek,Carlos A. Castro,Bahram Parvin,Jelena Kovacevic
IEEE Signal Processing Magazine. 2015; 32(1): 78
[Pubmed]  [Google Scholar] [DOI]
79 Blind colour separation of H&E stained histological images by linearly transforming the colour space
R. CELIS,D. ROMO,E. ROMERO
Journal of Microscopy. 2015; 260(3): 377
[Pubmed]  [Google Scholar] [DOI]
80 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]
81 A Nonlinear Mapping Approach to Stain Normalization in Digital Histopathology Images Using Image-Specific Color Deconvolution
Adnan Mujahid Khan,Nasir Rajpoot,Darren Treanor,Derek Magee
IEEE Transactions on Biomedical Engineering. 2014; 61(6): 1729
[Pubmed]  [Google Scholar] [DOI]
82 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]
83 Deep learning in neural networks: An overview
Jürgen Schmidhuber
Neural Networks. 2014;
[Pubmed]  [Google Scholar] [DOI]
84 Assessment of algorithms for mitosis detection in breast cancer histopathology images
Mitko Veta,Paul J. van Diest,Stefan M. Willems,Haibo Wang,Anant Madabhushi,Angel Cruz-Roa,Fabio Gonzalez,Anders B.L. Larsen,Jacob S. Vestergaard,Anders B. Dahl,Dan C. Cire?an,Jürgen Schmidhuber,Alessandro Giusti,Luca M. Gambardella,F. Boray Tek,Thomas Walter,Ching-Wei Wang,Satoshi Kondo,Bogdan J. Matuszewski,Frederic Precioso,Violet Snell,Josef Kittler,Teofilo E. de Campos,Adnan M. Khan,Nasir M. Rajpoot,Evdokia Arkoumani,Miangela M. Lacle,Max A. Viergever,Josien P.W. Pluim
Medical Image Analysis. 2014;
[Pubmed]  [Google Scholar] [DOI]
85 Exploring the Function of Cell Shape and Size during Mitosis
Clotilde Cadart,Ewa Zlotek-Zlotkiewicz,Maël Le Berre,Matthieu Piel,Helen K. Matthews
Developmental Cell. 2014; 29(2): 159
[Pubmed]  [Google Scholar] [DOI]
86 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]
87 Cell Words: Modelling the Visual Appearance of Cells inHistopathology Images
Adnan M. Khan,Korsuk Sirinukunwattana,Nasir M. Rajpoot
Computerized Medical Imaging and Graphics. 2014;
[Pubmed]  [Google Scholar] [DOI]

 

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