An investigation of XGBoost-based algorithm for breast cancer classification

[1]  Vijayan K. Asari,et al.  Breast Cancer Classification from Histopathological Images with Inception Recurrent Residual Convolutional Neural Network , 2018, Journal of Digital Imaging.

[2]  Ghulam Raza,et al.  Breast Cancer Multi-classification through Deep Neural Network and Hierarchical Classification Approach , 2019, Multimedia Tools and Applications.

[3]  J. R. Quinlan.  Induction of decision trees , 2004, Machine Learning.

[4]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[5]  Kilian Q. Weinberger,et al.  Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[6]  Karl Rohr,et al.  Predicting breast tumor proliferation from whole‐slide images: The TUPAC16 challenge , 2018, Medical Image Anal..

[7]  Francisco Herrera,et al.  BreakHis based breast cancer automatic diagnosis using deep learning: Taxonomy, survey and insights , 2020, Neurocomputing.

[8]  Kevin Smith,et al.  Digital image analysis in breast pathology-from image processing techniques to artificial intelligence. , 2017, Translational research : the journal of laboratory and clinical medicine.

[9]  Ziba Gandomkar,et al.  MuDeRN: Multi-category classification of breast histopathological image using deep residual networks , 2018, Artif. Intell. Medicine.

[10]  Zhihai Lu,et al.  Pathological brain detection based on AlexNet and transfer learning , 2019, J. Comput. Sci..

[11]  Ethem Alpaydin,et al.  Continuously Constructive Deep Neural Networks , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[12]  Joel J. P. C. Rodrigues,et al.  A novel deep learning based framework for the detection and classification of breast cancer using transfer learning , 2019, Pattern Recognit. Lett..

[13]  Luiz Eduardo Soares de Oliveira,et al.  Multiple instance learning for histopathological breast cancer image classification , 2019, Expert Syst. Appl..

[14]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[15]  Qisong Wu,et al.  Classification of Breast Cancer Histology Images Using Multi-Size and Discriminative Patches Based on Deep Learning , 2019, IEEE Access.

[16]  Sang-Woong Lee,et al.  Classification of breast cancer histology images using incremental boosting convolution networks , 2019, Inf. Sci..

[17]  อนิรุธ สืบสิงห์,et al.  Data Mining Practical Machine Learning Tools and Techniques , 2014 .

[18]  S. Singh,et al.  Deep learning-based model for breast cancer histopathology image classification , 2021, 2021 2nd International Conference on Intelligent Engineering and Management (ICIEM).

[19]  Vladimir Vapnik,et al.  Support-vector networks , 2004, Machine Learning.

[20]  Yann LeCun,et al.  Convolutional networks and applications in vision , 2010, Proceedings of 2010 IEEE International Symposium on Circuits and Systems.

[21]  Pendar Alirezazadeh,et al.  Representation learning-based unsupervised domain adaptation for classification of breast cancer histopathology images , 2018 .

[22]  Praveen Sankaran,et al.  Deep Learned Nucleus Features for Breast Cancer Histopathological Image Analysis based on Belief Theoretical Classifier Fusion , 2019, TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON).

[23]  Xiabi Liu,et al.  A new transfer learning based approach to magnification dependent and independent classification of breast cancer in histopathological images , 2021, Biomed. Signal Process. Control..

[24]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[25]  Kevin A. Schneider,et al.  Classification of Histopathological Biopsy Images Using Ensemble of Deep Learning Networks , 2019, CASCON.

[26]  Jack A Tuszynski,et al.  Automatic prediction of tumour malignancy in breast cancer with fractal dimension , 2016, Royal Society Open Science.

[27]  François Chollet,et al.  Xception: Deep Learning with Depthwise Separable Convolutions , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[28]  M. C. Hanumantharaju,et al.  Design of an Efficient Deep Neural Network for Multi-level Classification of Breast Cancer Histology Images , 2020 .

[29]  Meyke Hermsen,et al.  1399 H&E-stained sentinel lymph node sections of breast cancer patients: the CAMELYON dataset , 2018, GigaScience.

[30]  Juho Kannala,et al.  Deep learning for magnification independent breast cancer histopathology image classification , 2016, 2016 23rd International Conference on Pattern Recognition (ICPR).

[31]  Prashant Pathak,et al.  BREAST CANCER DETECTION USING ANN NETWORK AND PERFORMANCE ANALYSIS WITH SVM , 2019 .

[32]  John D. Pfeifer,et al.  Review of the current state of whole slide imaging in pathology , 2011, Journal of pathology informatics.

[33]  Serestina Viriri,et al.  Classification of Hematoxylin and Eosin-Stained Breast Cancer Histology Microscopy Images Using Transfer Learning with EfficientNets , 2021, Comput. Intell. Neurosci..

[34]  Zafer Cömert,et al.  BreastNet: A novel convolutional neural network model through histopathological images for the diagnosis of breast cancer , 2020 .

[35]  Andrew Evans,et al.  Digital imaging in pathology: whole-slide imaging and beyond. , 2013, Annual review of pathology.

[36]  Seetha Hari,et al.  Learning From Imbalanced Data , 2019, Advances in Computer and Electrical Engineering.

[37]  Min Beom Lee,et al.  Artificial Intelligence-Based Mitosis Detection in Breast Cancer Histopathology Images Using Faster R-CNN and Deep CNNs , 2020, Journal of clinical medicine.

[38]  Kun Zhang,et al.  Classification of Breast Cancer Based on Histology Images Using Convolutional Neural Networks , 2018, IEEE Access.

[39]  Taghi M. Khoshgoftaar,et al.  A survey on Image Data Augmentation for Deep Learning , 2019, Journal of Big Data.

[40]  Luiz Eduardo Soares de Oliveira,et al.  A Dataset for Breast Cancer Histopathological Image Classification , 2016, IEEE Transactions on Biomedical Engineering.

[41]  Xianhe Sun,et al.  Efficient Mitosis Detection in Breast Cancer Histology Images by RCNN , 2019, 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019).

[42]  Max Welling,et al.  Rotation Equivariant CNNs for Digital Pathology , 2018, MICCAI.

[43]  J. S. Marron,et al.  A method for normalizing histology slides for quantitative analysis , 2009, 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[44]  Catarina Eloy,et al.  BACH: Grand Challenge on Breast Cancer Histology Images , 2018, Medical Image Anal..

[45]  Luiz Eduardo Soares de Oliveira,et al.  Deep features for breast cancer histopathological image classification , 2017, 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[46]  J. Havel,et al.  Artificial neural networks in medical diagnosis , 2013 .

[47]  Gianpaolo Francesco Trotta,et al.  A performance comparison between shallow and deeper neural networks supervised classification of tomosynthesis breast lesions images , 2019, Cognitive Systems Research.

[48]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[49]  Maria S. Kulikova,et al.  Mitosis detection in breast cancer histological images An ICPR 2012 contest , 2013, Journal of pathology informatics.

[50]  Mohammed A. M. Abdullah,et al.  Going deeper: magnification-invariant approach for breast cancer classification using histopathological images , 2021, IET Comput. Vis..

[51]  Tin Kam Ho,et al.  The Random Subspace Method for Constructing Decision Forests , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[52]  Catarina Eloy,et al.  Classification of breast cancer histology images using Convolutional Neural Networks , 2017, PloS one.

[53]  Alexander Rakhlin,et al.  Deep Convolutional Neural Networks for Breast Cancer Histology Image Analysis , 2018, bioRxiv.

[54]  Cicilia R. M. Leite,et al.  Fuzzy method for pre-diagnosis of breast cancer from the Fine Needle Aspirate analysis , 2012, Biomedical engineering online.

[55]  Jyoti Parashar,et al.  Breast cancer images classification by clustering of ROI and mapping of features by CNN with XGBOOST learning , 2020 .

[56]  Qasem Abu Al-Haija,et al.  Breast Cancer Diagnosis in Histopathological Images Using ResNet-50 Convolutional Neural Network , 2020, 2020 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS).

[57]  Yuanjie Zheng,et al.  Breast Cancer Multi-classification from Histopathological Images with Structured Deep Learning Model , 2017, Scientific Reports.

[58]  George E. Dahl,et al.  Artificial Intelligence-Based Breast Cancer Nodal Metastasis Detection: Insights Into the Black Box for Pathologists. , 2018, Archives of pathology & laboratory medicine.

[59]  Luiz Eduardo Soares de Oliveira,et al.  Breast cancer histopathological image classification using Convolutional Neural Networks , 2016, 2016 International Joint Conference on Neural Networks (IJCNN).

[60]  Yasin Yari,et al.  A State-of-the-art Deep Transfer Learning-Based Model for Accurate Breast Cancer Recognition in Histology Images , 2020, 2020 IEEE 20th International Conference on Bioinformatics and Bioengineering (BIBE).

[61]  Andreas K. Maier,et al.  Classification of breast cancer histology images using transfer learning , 2018, ICIAR.

[62]  Hoo-Chang Hoo-Chang Shin Shin,et al.  Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning , 2016, Ieee Transactions on Medical Imaging.

[63]  Zafer Cömert,et al.  Computer-aided diagnosis system combining FCN and Bi-LSTM model for efficient breast cancer detection from histopathological images , 2019, Appl. Soft Comput..

[64]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[65]  Bram van Ginneken,et al.  The importance of stain normalization in colorectal tissue classification with convolutional networks , 2017, 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017).