An Efficient Lightweight CNN and Ensemble Machine Learning Classification of Prostate Tissue Using Multilevel Feature Analysis

[1]  Franco Turini,et al.  A Survey of Methods for Explaining Black Box Models , 2018, ACM Comput. Surv..

[2]  W. Grizzle,et al.  A standard tissue as a control for histochemical and immunohistochemical staining , 2016, Biotechnic & histochemistry : official publication of the Biological Stain Commission.

[3]  Yukako Yagi,et al.  Staining Correction in Digital Pathology by Utilizing a Dye Amount Table , 2015, Journal of Digital Imaging.

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

[5]  David S. Wishart,et al.  Applications of Machine Learning in Cancer Prediction and Prognosis , 2006, Cancer informatics.

[6]  Guoping Qiu,et al.  Learning Based Image Transformation Using Convolutional Neural Networks , 2018, IEEE Access.

[7]  Francesco Di Maria,et al.  Classification of Tissue Regions in Histopathological Images: Comparison Between Pre-trained Convolutional Neural Networks and Local Binary Patterns Variants , 2020 .

[8]  Masoom A. Haider,et al.  Prostate Cancer Detection using Deep Convolutional Neural Networks , 2019, Scientific Reports.

[9]  M. K. Hosain,et al.  Classification of malignant and benign tissue with logistic regression , 2019, Informatics in Medicine Unlocked.

[10]  Zhihong Huang,et al.  Prediction of prostate cancer Gleason score upgrading from biopsy to radical prostatectomy using pre-biopsy multiparametric MRI PIRADS scoring system , 2020, Scientific Reports.

[11]  June-Goo Lee,et al.  Deep Learning in Medical Imaging: General Overview , 2017, Korean journal of radiology.

[12]  Oliver Grimm,et al.  Enabling Histopathological Annotations on Immunofluorescent Images through Virtualization of Hematoxylin and Eosin , 2018, Journal of pathology informatics.

[13]  Madhu S. Nair,et al.  Automated grading of prostate cancer using convolutional neural network and ordinal class classifier , 2019, Informatics in Medicine Unlocked.

[14]  Minsue T. Chen,et al.  Hematoxylin and Eosin Tissue Stain in Mohs Micrographic Surgery: A Review , 2011, Dermatologic surgery : official publication for American Society for Dermatologic Surgery [et al.].

[15]  Zuhair M Mohammedsaleh,et al.  Histological Stains: A Literature Review and Case Study , 2015, Global journal of health science.

[16]  Emmanuel Adetiba,et al.  Medical Image Classification with Hand-Designed or Machine-Designed Texture Descriptors: A Performance Evaluation , 2018, IWBBIO.

[17]  Lucas C Cahill,et al.  Comparing histologic evaluation of prostate tissue using nonlinear microscopy and paraffin H&E: a pilot study , 2019, Modern Pathology.

[18]  Paolo Napoletano,et al.  Improved opponent color local binary patterns: an effective local image descriptor for color texture classification , 2017, J. Electronic Imaging.

[19]  K Vyrsokinos,et al.  An all-optical neuron with sigmoid activation function. , 2019, Optics express.

[20]  Seung Hwan Lee,et al.  Pathological Characteristics of Prostate Cancer in Men Aged < 50 Years Treated with Radical Prostatectomy: a Multi-Centre Study in Korea , 2019, Journal of Korean medical science.

[21]  T. H. van der Kwast,et al.  Guidelines for processing and reporting of prostatic needle biopsies , 2003, Journal of clinical pathology.

[22]  J. Seuntjens,et al.  Deep learning in head & neck cancer outcome prediction , 2019, Scientific Reports.

[23]  Joakim Lindblad,et al.  Blind Color Decomposition of Histological Images , 2013, IEEE Transactions on Medical Imaging.

[24]  P. Humphrey,et al.  Diagnosis of adenocarcinoma in prostate needle biopsy tissue , 2007, Journal of Clinical Pathology.

[25]  Shih-Chia Huang,et al.  Efficient Contrast Enhancement Using Adaptive Gamma Correction With Weighting Distribution , 2013, IEEE Transactions on Image Processing.

[26]  G. Andriole,et al.  Improved biopsy efficiency with MR/ultrasound fusion-guided prostate biopsy. , 2016, Journal of the National Cancer Institute.

[27]  Qiuyu Zhu,et al.  Improving Classification Performance of Softmax Loss Function Based on Scalable Batch-Normalization , 2020, Applied Sciences.

[28]  Amina A. Qutub,et al.  Image-based Classification of Tumor Type and Growth Rate using Machine Learning: a preclinical study , 2019, Scientific Reports.

[29]  Oleg Pustovyy,et al.  Primo-Vascular System as Presented by Bong Han Kim , 2015, Evidence-based complementary and alternative medicine : eCAM.

[30]  Max A. Viergever,et al.  Breast Cancer Histopathology Image Analysis: A Review , 2014, IEEE Transactions on Biomedical Engineering.

[31]  Raymond Y Huang,et al.  Artificial intelligence in cancer imaging: Clinical challenges and applications , 2019, CA: a cancer journal for clinicians.

[32]  E. Topol,et al.  Adapting to Artificial Intelligence: Radiologists and Pathologists as Information Specialists. , 2016, JAMA.

[33]  Berkman Sahiner,et al.  Deep learning in medical imaging and radiation therapy. , 2018, Medical physics.

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

[35]  George Lee,et al.  Image analysis and machine learning in digital pathology: Challenges and opportunities , 2016, Medical Image Anal..

[36]  Huai Li,et al.  Artificial convolution neural network for medical image pattern recognition , 1995, Neural Networks.

[37]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

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

[39]  Ellery Wulczyn,et al.  Development and validation of a deep learning algorithm for improving Gleason scoring of prostate cancer , 2018, npj Digital Medicine.

[40]  Loris Nanni,et al.  Handcrafted vs. non-handcrafted features for computer vision classification , 2017, Pattern Recognit..

[41]  Cuong Nguyen,et al.  Random forest classifier combined with feature selection for breast cancer diagnosis and prognostic , 2013 .

[42]  Thomas G. Dietterich Multiple Classifier Systems , 2000, Lecture Notes in Computer Science.

[43]  Purang Abolmaesumi,et al.  Computer-aided diagnosis of prostate cancer with emphasis on ultrasound-based approaches: a review. , 2007, Ultrasound in medicine & biology.

[44]  Mark D Zarella,et al.  An alternative reference space for H&E color normalization , 2017, PloS one.

[45]  R. Kumar,et al.  Detection and Classification of Cancer from Microscopic Biopsy Images Using Clinically Significant and Biologically Interpretable Features , 2015, Journal of medical engineering.

[46]  Feng Li,et al.  Deep learning for irregularly and regularly missing data reconstruction , 2020, Scientific Reports.

[47]  S. Freedland,et al.  Prostate Cancer and Prostatic Diseases Best of Asia, 2019: challenges and opportunities , 2019, Prostate Cancer and Prostatic Diseases.

[48]  P. Humphrey,et al.  Gleason grading and prognostic factors in carcinoma of the prostate , 2004, Modern Pathology.

[49]  Richard K. G. Do,et al.  Convolutional neural networks: an overview and application in radiology , 2018, Insights into Imaging.

[50]  T. H. van der Kwast,et al.  EAU guidelines on prostate cancer. part 1: screening, diagnosis, and local treatment with curative intent-update 2013. , 2014, European urology.

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

[52]  Yoichi Hayashi,et al.  New unified insights on deep learning in radiological and pathological images: Beyond quantitative performances to qualitative interpretation , 2020 .

[53]  Stuart A. Taylor,et al.  Logistic regression model for diagnosis of transition zone prostate cancer on multi-parametric MRI , 2014, European Radiology.

[54]  Matthew T. Freedman,et al.  Artificial convolution neural network techniques and applications for lung nodule detection , 1995, IEEE Trans. Medical Imaging.

[55]  Kenji Doya,et al.  Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learning , 2017, Neural Networks.

[56]  Constantino Carlos Reyes-Aldasoro,et al.  Evaluation of Colour Pre-processing on Patch-Based Classification of H&E-Stained Images , 2019, ECDP.

[57]  Shanto Rahman,et al.  An adaptive gamma correction for image enhancement , 2016, EURASIP J. Image Video Process..

[58]  Stephen A. Smith,et al.  Characterization of the histologic appearance of normal gill tissue using special staining techniques , 2018, Journal of veterinary diagnostic investigation : official publication of the American Association of Veterinary Laboratory Diagnosticians, Inc.

[59]  Arvid Lundervold,et al.  An overview of deep learning in medical imaging focusing on MRI , 2018, Zeitschrift fur medizinische Physik.