BishtRef
Articles Journals Publishers Authors Subjects Most Cited New Papers Year Stats Open Access
journal article Nov 21, 2021

Deep learning in histopathology: A review

Sugata Banerji ORCID Sushmita Mitra ORCID
WIREs Data Mining and Knowledge Discovery Vol. 12 No. 1 · Wiley
View at Publisher Save 10.1002/widm.1439
Abstract
AbstractHistopathology is diagnosis based on visual examination of tissue sections under a microscope. With the growing number of digitally scanned tissue slide images, computer‐based segmentation and classification of these images is a high‐demand area of research. Convolutional neural networks (CNNs) constitute the most popular classification architecture for a variety of image classification problems. However, applying CNNs to histology slides is not a trivial task and has several challenges, ranging from variations in the colors of slides to excessive high resolution and lack of proper labeling. In this advanced review, we introduce the application of CNN‐based architectures to digital histological image analysis, discuss some problems associated with such analysis, and look at possible solutions.This article is categorized under:
Application Areas > Health Care
Fundamental Concepts of Data and Knowledge > Big Data Mining
Technologies > Machine Learning
Topics

No keywords indexed for this article. Browse by subject →

References
47
[6]
Ciompi F. Geessink O. Bejnordi B. E. deSouza G. S. Baidoshvili A. Litjens G. vanGinneken B. Nagtegaal I.&van derLaak J.(2017 April). The importance of stain normalization in colorectal tissue classification with convolutional networks. 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017). IEEE. pp. 160–163.https://doi.org/10.1109/isbi.2017.7950492 10.1109/isbi.2017.7950492
[7]
Das K. Conjeti S. Guha Roy A. Chatterjee J.&Sheet D.(2018). Multiple‐instance learning of deep convolutional neural networks for breast histopathology whole slide classification. Proceedings of IEEE 15th international symposium on biomedical imaging (ISBI 2018) Washington D.C USA. pp. 578–581. 10.1109/isbi.2018.8363642
[8]
Solving the multiple instance problem with axis-parallel rectangles

Thomas G. Dietterich, Richard H. Lathrop, Tomás Lozano-Pérez

Artificial Intelligence 10.1016/s0004-3702(96)00034-3
[10]
Genomic data commons data portal (legacy archive). (n.d.). Available fromhttps://portal.gdc.cancer.gov/legacy-archive/.
[11]
Glorot X. Bordes A.&Bengio Y.(2011). Deep sparse rectifier neural networks. Proceedings of 14th International Conference on Artificial Intelligence and Statistics Ft. Lauderdale FL USA. pp. 315–323.
[12]
Gupta V. &Bhavsar A.(2017 Jul).Breast cancer histopathological image classification: Is magnification important?2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE. pp. 769–776.https://doi.org/10.1109/cvprw.2017.107 10.1109/cvprw.2017.107
[14]
Hou L. Samaras D. KurcT.M. GaoY. DavisJ.E.&SaltzJ.H.(2016). Patch‐based convolutional neural network for whole slide tissue image classification. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Las Vegas NV USA. pp. 2424–2433. 10.1109/cvpr.2016.266
[16]
Kieffer B. Babaie M. Kalra S.&Tizhoosh H. R.(2017 November). Convolutional neural networks for histopathology image classification: Training vs. using pre‐trained networks. 2017 Seventh International Conference on Image Processing Theory Tools and Applications (IPTA) Montreal Canada. IEEE.https://doi.org/10.1109/ipta.2017.8310149 10.1109/ipta.2017.8310149
[18]
Krizhevsky A. Sutskever I.&Hinton G. E.(2012). ImageNet classification with deep convolutional neural networks. Proceedings of the Twenty‐sixth Conference on Neural Information Processing Systems Lake Tahoe CA USA. pp. 1106–1114.
[21]
Deep learning

Yann LeCun, Yoshua Bengio, Geoffrey Hinton

Nature 10.1038/nature14539
[22]
A survey on deep learning in medical image analysis

Geert Litjens, Thijs Kooi, Babak Ehteshami Bejnordi et al.

Medical Image Analysis 10.1016/j.media.2017.07.005
[23]
Liu Y. Gadepalli K. Nourozi M. Dahl G. E. Kohlberger T. Boyko A. Venugopalan S. Timofeev A. Nelson P. Q. Corrado G. S. Hipp J. D. Peng L.&Stumpe M. C.(2017). Detecting cancer metastases on gigapixel pathology images. CoRR abs/1703.02442.
[24]
The Genotype-Tissue Expression (GTEx) project

John Lonsdale, Jeffrey Thomas, Mike Salvatore et al.

Nature Genetics 10.1038/ng.2653
[34]
Simonyan K.&Zisserman A.(2014). Very deep convolutional networks for large‐scale image recognition. Proceedings International Conference on Learning Representations Banff Canada.
[35]
Accurate Segmentation of Cervical Cytoplasm and Nuclei Based on Multiscale Convolutional Network and Graph Partitioning

Youyi Song, Libing Zhang, Siping Chen et al.

IEEE Transactions on Biomedical Engineering 10.1109/tbme.2015.2430895
[37]
Srivastava N. "Dropout: A simple way to prevent neural networks from overfitting" Journal of Machine Learning Research (2014)
[38]
Wang C. Shi J. Zhang Q.&Ying S.(2017). Histopathological image classification with bilinear convolutional neural networks. 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) Jeju Island Korea. IEEE.https://doi.org/10.1109/embc.2017.8037745 10.1109/embc.2017.8037745
[40]
Comprehensive analysis of lung cancer pathology images to discover tumor shape and boundary features that predict survival outcome

Shidan Wang, Alyssa Chen, Lin Yang et al.

Scientific Reports 10.1038/s41598-018-27707-4
[41]
Wu H. Phan J. H. Bhatia A. K. Cundiff C. A. Shehata B. M.&Wang M. D.(2015 Aug). Detection of blur artifacts in histopathological whole‐slide images of endomyocardial biopsies. 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) Milan Italy. IEEE. pp. 727–730.https://doi.org/10.1109/embc.2015.7318465 10.1109/embc.2015.7318465
[42]
Xu Y. Jia Z. Ai Y. Zhang F. Lai M.&Chang E. I. C.(2015). Deep convolutional activation features for large scale brain tumor histopathology image classification and segmentation. Proceedings of International Conference on Acoustics Speech and Signal Processing (ICASSP) South Brisbane QLD Australia 2015. IEEE. pp. 947–951. 10.1109/icassp.2015.7178109
[47]
Zhou Y. Chang H. Barner K. Spellman P.&Bahram P.(2014 June). Classification of histology sections via multispectral convolutional sparse coding. Proceedings of Conference Computer Vision Pattern Recognition Workshops Columbus OH USA (pp. 3081–3088).https://doi.org/10.1109/CVPR.2014.394. 10.1109/cvpr.2014.394
Cited By
42
A Survey on Explainable Artificial Intelligence (XAI) Techniques for Visualizing Deep Learning Models in Medical Imaging

Deepshikha Bhati, Fnu Neha · 2024

Journal of Imaging
Metrics
42
Citations
47
References
Details
Published
Nov 21, 2021
Vol/Issue
12(1)
License
View
Authors
S
Sugata Banerji

Department of Mathematics and Computer Science Lake Forest College Lake Forest Illinois USA

S
Sushmita Mitra

Machine Intelligence Unit Indian Statistical Institute Kolkata India

Cite This Article
Sugata Banerji, Sushmita Mitra (2021). Deep learning in histopathology: A review. WIREs Data Mining and Knowledge Discovery, 12(1). https://doi.org/10.1002/widm.1439
Related

You May Also Like

Ensemble learning: A survey

Omer Sagi, Lior Rokach · 2018

2,146 citations

Classification and regression trees

Wei‐Yin Loh · 2011

1,781 citations

Hyperparameters and tuning strategies for random forest

Philipp Probst, Marvin N. Wright · 2019

1,369 citations

Educational data mining and learning analytics: An updated survey

Cristobal Romero, Sebastián Ventura · 2020

663 citations

Bias in data‐driven artificial intelligence systems—An introductory survey

Eirini Ntoutsi, Pavlos Fafalios · 2020

652 citations