Machine learning approaches for pathologic diagnosis

Machine learning techniques, especially deep learning techniques such as convolutional neural networks, have been successfully applied to general image recognitions since their overwhelming performance at the 2012 ImageNet Large Scale Visual Recognition Challenge. Recently, such techniques have also been applied to various medical, including histopathological, images to assist the process of medical diagnosis. In some cases, deep learning–based algorithms have already outperformed experienced pathologists for recognition of histopathological images. However, pathological images differ from general images in some aspects, and thus, machine learning of histopathological images requires specialized learning methods. Moreover, many pathologists are skeptical about the ability of deep learning technology to accurately recognize histopathological images because what the learned neural network recognizes is often indecipherable to humans. In this review, we first introduce various applications incorporating machine learning developed to assist the process of pathologic diagnosis, and then describe machine learning problems related to histopathological image analysis, and review potential ways to solve these problems.

[1]  Marcus Liwicki,et al.  Bayesian Convolutional Neural Networks with Variational Inference , 2018, 1806.05978.

[2]  Jonathan Krause,et al.  Grader variability and the importance of reference standards for evaluating machine learning models for diabetic retinopathy , 2017, Ophthalmology.

[3]  Anant Madabhushi,et al.  Content-based image retrieval of digitized histopathology in boosted spectrally embedded spaces , 2015, Journal of pathology informatics.

[4]  Guoping Qiu,et al.  An End-to-End Deep Learning Histochemical Scoring System for Breast Cancer TMA , 2018, IEEE Transactions on Medical Imaging.

[5]  Jaime S. Cardoso,et al.  Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support , 2017, Lecture Notes in Computer Science.

[6]  Lin Yang,et al.  Robust Nucleus/Cell Detection and Segmentation in Digital Pathology and Microscopy Images: A Comprehensive Review , 2016, IEEE Reviews in Biomedical Engineering.

[7]  Svitlana Zinger,et al.  Histopathology stain-color normalization using deep generative models , 2018 .

[8]  Nasir M. Rajpoot,et al.  A Nonlinear Mapping Approach to Stain Normalization in Digital Histopathology Images Using Image-Specific Color Deconvolution , 2014, IEEE Transactions on Biomedical Engineering.

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

[10]  Joel H. Saltz,et al.  Sparse Autoencoder for Unsupervised Nucleus Detection and Representation in Histopathology Images , 2017, Pattern Recognit..

[11]  Jaehoon Lee,et al.  Bayesian Convolutional Neural Networks with Many Channels are Gaussian Processes , 2018, ArXiv.

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

[13]  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).

[14]  Lin Yang,et al.  Content-based histopathology image retrieval using CometCloud , 2014, BMC Bioinformatics.

[15]  Nico Karssemeijer,et al.  Stain Specific Standardization of Whole-Slide Histopathological Images , 2016, IEEE Transactions on Medical Imaging.

[16]  Hao Chen,et al.  ScanNet: A Fast and Dense Scanning Framework for Metastastic Breast Cancer Detection from Whole-Slide Image , 2017, 2018 IEEE Winter Conference on Applications of Computer Vision (WACV).

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

[18]  Konstantinos N. Plataniotis,et al.  A Complete Color Normalization Approach to Histopathology Images Using Color Cues Computed From Saturation-Weighted Statistics , 2015, IEEE Transactions on Biomedical Engineering.

[19]  Utilizing machine learning to discern hidden clinical values from big data in urology , 2020, Investigative and clinical urology.

[20]  Arnav Bhavsar,et al.  Breast Cancer Histopathological Image Classification: Is Magnification Important? , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[21]  Fabio A. González,et al.  Content-based histopathology image retrieval using a kernel-based semantic annotation framework , 2011, J. Biomed. Informatics.

[22]  Joel H. Saltz,et al.  Unsupervised Histopathology Image Synthesis , 2017, ArXiv.

[23]  Neeraj Kumar,et al.  Empirical comparison of color normalization methods for epithelial-stromal classification in H and E images , 2016, Journal of pathology informatics.

[24]  Jill S Barnholtz-Sloan,et al.  Predicting cancer outcomes from histology and genomics using convolutional networks , 2017 .

[25]  Luca Maria Gambardella,et al.  Assessment of algorithms for mitosis detection in breast cancer histopathology images , 2014, Medical Image Anal..

[26]  Andrew H. Beck,et al.  Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer , 2017, JAMA.

[27]  Nassir Navab,et al.  Staingan: Stain Style Transfer for Digital Histological Images , 2018, 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019).

[28]  Vipin Chaudhary,et al.  Content based sub-image retrieval system for high resolution pathology images using salient interest points , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[29]  Wei Wei,et al.  Thoracic Disease Identification and Localization with Limited Supervision , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[30]  Yijing Li,et al.  Learning from class-imbalanced data: Review of methods and applications , 2017, Expert Syst. Appl..

[31]  Percy Liang,et al.  Understanding Black-box Predictions via Influence Functions , 2017, ICML.

[32]  Thomas Brox,et al.  Striving for Simplicity: The All Convolutional Net , 2014, ICLR.

[33]  Nassir Navab,et al.  Structure-Preserving Color Normalization and Sparse Stain Separation for Histological Images , 2016, IEEE Transactions on Medical Imaging.

[34]  Yoshua Bengio,et al.  Generative Adversarial Networks , 2014, ArXiv.

[35]  G. Corrado,et al.  Using a Deep Learning Algorithm and Integrated Gradients Explanation to Assist Grading for Diabetic Retinopathy. , 2019, Ophthalmology.

[36]  Constantine Bekas,et al.  BAGAN: Data Augmentation with Balancing GAN , 2018, ArXiv.

[37]  Zoubin Ghahramani,et al.  Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference , 2015, ArXiv.

[38]  Hao Chen,et al.  DCAN: Deep Contour-Aware Networks for Accurate Gland Segmentation , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[39]  Daisuke Komura,et al.  Machine Learning Methods for Histopathological Image Analysis , 2017, Computational and structural biotechnology journal.

[40]  Lawrence Carin,et al.  An Active Learning Approach for Rapid Characterization of Endothelial Cells in Human Tumors , 2014, PloS one.

[41]  Hao Chen,et al.  Gland segmentation in colon histology images: The glas challenge contest , 2016, Medical Image Anal..

[42]  Anne L. Martel,et al.  Clustering Analysis for Semi-supervised Learning Improves Classification Performance of Digital Pathology , 2015, MLMI.

[43]  Fang Liu,et al.  Bayesian convolutional neural network based MRI brain extraction on nonhuman primates , 2018, NeuroImage.

[44]  Anant Madabhushi,et al.  An active learning based classification strategy for the minority class problem: application to histopathology annotation , 2011, BMC Bioinformatics.

[45]  Michael S. Bernstein,et al.  ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.

[46]  Atsuto Maki,et al.  A systematic study of the class imbalance problem in convolutional neural networks , 2017, Neural Networks.