Informatics, digital & computational pathology
Computational pathology
Computational pathology fundamentals & applications

Editor-in-Chief: Debra L. Zynger, M.D.
Cleopatra Kozlowski, Ph.D.

Topic Completed: 6 December 2019

Minor changes: 3 March 2021

Copyright: 2019-2021,, Inc.

PubMed Search: Computational pathology [title]

Cleopatra Kozlowski, Ph.D.
Page views in 2020: 692
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Cite this page: Kozlowski C. Computational pathology fundamentals & applications. website. Accessed May 11th, 2021.
Definition / general
  • A branch of pathology that involves extraction of information from digitized pathology images in combination with their associated metadata, typically using artificial intelligence methods such as deep learning
Essential features
  • Computational pathology is the analysis of digitized pathology images with associated metadata, typically using artificial intelligence (AI) methods
  • Deep learning is a type of AI method, commonly used in computational pathology, that is able to "learn" how to perform tasks based on examples
  • Training a typical supervised deep learning algorithm in computational pathology involves large amounts of labeled training data
  • Artificial intelligence (AI): branch of computer science dealing with simulation of intelligent behavior in computers
  • Cloud computing: using a network of remote servers to store, manage and process data
  • Convolutional neural network (CNN): type of deep neural network particularly designed for images
  • Deep learning: complex neural networks where software trains itself to perform tasks
  • Machine learning: branch of AI where software learns to perform tasks by being exposed to representative data
  • Supervised machine learning: training based on labels associated with a data point (i.e. ground truth)
  • Unsupervised machine learning: training based on natural divisions in a data set without the need for a ground truth, e.g. clustering methods
  • Reference: J Pathol 2019;249:286
Diagrams / tables

Traditional image analysis versus computational pathology
“Traditional” image analysis
(J Pathol Inform 2019;10:9)
Deep learning powered
computational pathology
(J Pathol 2019;249:286)
Typical tasks Automation of repetitive tasks, e.g.:
  • cell counting
  • stain quantification
Image features correlated to
patient meta data in order to, e.g.:
  • diagnose disease
  • predict treatment response
Parameter tuning Image features / parameters are
manually tuned
Algorithm learns and extracts
a large number of features
Typical algorithm testing Often on a few regions of the slide Usually whole slide
Computer unit best suited for task CPU (central processing unit) GPU (graphics processing unit)
Number of training images required Depending on application, may be low Usually very high
Basic considerations for supervised deep learning algorithm development
  • Obtaining ground truth data
    • Patient outcome data
    • A field from the pathology report or laboratory information system
    • A quantitative score assigned to the case
    • Manually provided by a pathologist
    • Considerations in acquisition
      • Streamlined workflows
      • Single common annotation tool
      • Tradeoff between quantity and accuracy
  • Good practices
    • Training images must be representative of the images algorithm is designed to be applied to
    • Use a wide variety of data sources
    • Use consistent pre-imaging steps
    • Apply manual or automated image quality control processes
    • Use larger and more representative training sets
    • Calibrate algorithms for each lab prior to being used for clinical work
    • Apply image preprocessing strategies such as color normalization
    • Data augmentation to artificially add variation and increase (or balance) the training data
    • Test developed models using a variety of test and validation sets to avoid overfitting
Examples of uses of computational pathology
  • "Predicting cancer outcomes from histology and genomics using convolutional networks" (Proc Natl Acad Sci U S A 2018;115:E2970)
    • Used information from histology images and genomic biomarkers to predict survival in patients with glioma
    • Combined convolutional neural network with survival models to predict time-to-event data
    • Heatmap visualization to show image features related to prognosis
  • "Automated Gleason grading of prostate cancer tissue microarrays via deep learning" (Sci Rep 2018;8:12054)
    • Used deep learning to train an algorithm to grade prostate cancer
    • Used annotated tissue microarray images of prostate cancer to train algorithm
    • Used transfer learning (network trained on another task and added their own images) so that they could build an algorithm with only 641 patients, a relatively small sample size
    • Used "class activation mapping" to confirm that the model was focusing on important image features
Board review style question #1
    Which of the following statements could be true for a deep learning based algorithm but typically not for a traditional image analysis algorithm?

  1. The algorithm can segment and classify objects in H&E images
  2. The algorithm uses handcrafted image features, such as cell shape, to classify objects
  3. The algorithm "learns" by extracting important image features automatically from examples
  4. The algorithm can measure staining intensity of labeled objects from IHC images
Board review style answer #1
C. The algorithm "learns" by extracting important image features automatically from examples

Explanation: C is the correct answer as it is the primary feature of deep learning based algorithms but not of traditional image analysis algorithms. A is a task that can be accomplished by both types of methods. B and D are commonly performed by traditional image analysis algorithms.

Reference: Computational pathology fundamentals

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Board review style question #2
    Which of the following statements about supervised deep learning is true?

  1. Images used for training must be representative of the images the algorithm is designed to be applied to
  2. To reduce bias, images used to train algorithms should be carefully selected to contain minimal artifacts
  3. Because deep learning is very powerful, only a small number of images is typically required for training
  4. Trained algorithms should be tested in datasets that were also used for training
Board review style answer #2
A. Images used for training must be representative of the images the algorithm is designed to be applied to

Explanation: A is the only true statement. B is false because images should contain artifacts represented in the images the algorithm is designed to be applied to. C is false because deep learning typically requires a large number of images to train. D is false because trained algorithms need to be tested in an entirely independent dataset.

Reference: Computational pathology fundamentals

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