Hierarchical Fused Model With Deep Learning and Type-2 Fuzzy Learning for Breast Cancer Diagnosis
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Fei-Yue Wang | Chao Gou | Tianyu Shen | Jiangong Wang | Chao Gou | Fei-Yue Wang | Jiangong Wang | Tianyu Shen
[1] Fei-Yue Wang,et al. Simultaneous Segmentation and Classification of Mass Region From Mammograms Using a Mixed-Supervision Guided Deep Model , 2020, IEEE Signal Processing Letters.
[2] Hon Keung Kwan,et al. A fuzzy neural network and its application to pattern recognition , 1994, IEEE Trans. Fuzzy Syst..
[3] Yi Ding,et al. RUN: Residual U-Net for Computer-Aided Detection of Pulmonary Nodules without Candidate Selection , 2018, ArXiv.
[4] Ling Shao,et al. Collaborative Learning of Semi-Supervised Segmentation and Classification for Medical Images , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[5] Milos Manic,et al. General Type-2 Fuzzy C-Means Algorithm for Uncertain Fuzzy Clustering , 2012, IEEE Transactions on Fuzzy Systems.
[6] J. Buckley,et al. Fuzzy neural networks: a survey , 1994 .
[7] Lotfi A. Zadeh,et al. The concept of a linguistic variable and its application to approximate reasoning-III , 1975, Inf. Sci..
[8] Andrew P. Bradley,et al. Automated Analysis of Unregistered Multi-View Mammograms With Deep Learning , 2017, IEEE Transactions on Medical Imaging.
[9] James C. Bezdek,et al. Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.
[10] Kuo-Lung Wu,et al. Unsupervised possibilistic clustering , 2006, Pattern Recognit..
[11] Rangaraj M. Rangayyan,et al. Application of shape analysis to mammographic calcifications , 1994, IEEE Trans. Medical Imaging.
[12] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[13] Oscar Castillo,et al. An Extension of the Fuzzy Possibilistic Clustering Algorithm Using Type-2 Fuzzy Logic Techniques , 2017, Adv. Fuzzy Syst..
[14] L. A. ZADEH,et al. The concept of a linguistic variable and its application to approximate reasoning - I , 1975, Inf. Sci..
[15] Yang Chen,et al. Studies on Centroid Type-Reduction Algorithms for Interval Type-2 Fuzzy Logic Systems , 2015, 2015 IEEE Fifth International Conference on Big Data and Cloud Computing.
[16] Jaime S. Cardoso,et al. INbreast: toward a full-field digital mammographic database. , 2012, Academic radiology.
[17] Madan M. Gupta,et al. On the principles of fuzzy neural networks , 1994 .
[18] Gustavo Carneiro,et al. Deep Learning and Structured Prediction for the Segmentation of Mass in Mammograms , 2015, MICCAI.
[19] Ulas Bagci,et al. TumorNet: Lung nodule characterization using multi-view Convolutional Neural Network with Gaussian Process , 2017, 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017).
[20] Saeid Nahavandi,et al. Deep imitation learning for autonomous vehicles based on convolutional neural networks , 2020, IEEE/CAA Journal of Automatica Sinica.
[21] Xiaoming Liu,et al. Mass Classification in Mammograms Using Selected Geometry and Texture Features, and a New SVM-Based Feature Selection Method , 2014, IEEE Systems Journal.
[22] A. Jemal,et al. Cancer statistics, 2018 , 2018, CA: a cancer journal for clinicians.
[23] Guang-ming Xian,et al. An identification method of malignant and benign liver tumors from ultrasonography based on GLCM texture features and fuzzy SVM , 2010, Expert Syst. Appl..
[24] Robert LIN,et al. NOTE ON FUZZY SETS , 2014 .
[25] Xuelong Li,et al. Mammographic mass segmentation: Embedding multiple features in vector-valued level set in ambiguous regions , 2011, Pattern Recognit..
[26] Mert R. Sabuncu,et al. Anatomical Priors in Convolutional Networks for Unsupervised Biomedical Segmentation , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[27] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[28] Mohammad Hossein Fazel Zarandi,et al. Type-II Fuzzy Possibilistic C-Mean Clustering , 2009, IFSA/EUSFLAT Conf..
[29] Khalid M. Amin,et al. A novel breast tumor classification algorithm using neutrosophic score features , 2016 .
[30] Paul Wintz,et al. Digital image processing (2nd ed.) , 1987 .
[31] Fei-Yue Wang,et al. Application of Interval Type-2 Fuzzy Sets in Unmanned Vehicle Visual Guidance , 2019, Int. J. Fuzzy Syst..
[32] Geoffrey E. Hinton,et al. On the importance of initialization and momentum in deep learning , 2013, ICML.
[33] Hee Chan Kim,et al. Computer-aided diagnosis of solid breast nodules: use of an artificial neural network based on multiple sonographic features , 2004, IEEE Trans. Medical Imaging.
[34] Frank Chung-Hoon Rhee,et al. Uncertain Fuzzy Clustering: Interval Type-2 Fuzzy Approach to $C$-Means , 2007, IEEE Transactions on Fuzzy Systems.
[35] Trevor Darrell,et al. Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[36] Jyh-Shing Roger Jang,et al. ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..
[37] Yilong Yin,et al. Breast masses in mammography classification with local contour features , 2017, Biomedical engineering online.
[38] Lingxi Li,et al. Type-2 fuzzy control for driving state and behavioral decisions of unmanned vehicle , 2020, IEEE/CAA Journal of Automatica Sinica.
[39] Fei-Yue Wang,et al. Linguistic Dynamic Analysis and Evaluation Based on Partially Connected Type-2 Fuzzy Sets , 2019, Int. J. Fuzzy Syst..
[40] Francisco Herrera,et al. BreakHis based breast cancer automatic diagnosis using deep learning: Taxonomy, survey and insights , 2020, Neurocomputing.
[41] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[42] Xiao Wang,et al. Type-2 Fuzzy Comprehension Evaluation for Tourist Attractive Competency , 2019, IEEE Transactions on Computational Social Systems.
[43] Tao Li,et al. Lesion Detection and Grading of Diabetic Retinopathy via Two-Stages Deep Convolutional Neural Networks , 2017, MICCAI.
[44] A. Stavros,et al. Solid breast nodules: use of sonography to distinguish between benign and malignant lesions. , 1995, Radiology.
[45] Ritse Mann,et al. Automated soft tissue lesion detection and segmentation in digital mammography using a u-net deep learning network , 2018, ArXiv.
[46] Ulas Bagci,et al. Deep learning beyond cats and dogs: recent advances in diagnosing breast cancer with deep neural networks. , 2018, The British journal of radiology.
[47] Marek Kowal,et al. Feature selection for breast cancer malignancy classification problem , 2010 .
[48] Roberto Cipolla,et al. SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[49] Sim Heng Ong,et al. Focus, Segment and Erase: An Efficient Network for Multi-label Brain Tumor Segmentation , 2018, ECCV.
[50] Thomas Brox,et al. U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.
[51] Eka Miranda,et al. A survey of medical image classification techniques , 2016, 2016 International Conference on Information Management and Technology (ICIMTech).
[52] M. Sugeno,et al. Derivation of Fuzzy Control Rules from Human Operator's Control Actions , 1983 .
[53] H.M. Wechsler,et al. Digital image processing, 2nd ed. , 1981, Proceedings of the IEEE.
[54] Jie Zhu,et al. Shearlet-based texture feature extraction for classification of breast tumor in ultrasound image , 2013, Biomed. Signal Process. Control..
[55] Gustavo Carneiro,et al. A deep learning approach for the analysis of masses in mammograms with minimal user intervention , 2017, Medical Image Anal..