This document provides an overview of artificial intelligence in pathology. It discusses digital pathology and whole slide imaging, which allow pathologists to view digital images of tissue slides. Artificial intelligence techniques like deep learning can be applied to these digital images for tasks like classification, segmentation, and image analysis. Specifically, AI has applications in pathology for detecting features in images like mitotic figures, quantifying biomarkers, and assisting with cancer grading. Studies have shown AI can help pathologists by improving accuracy, efficiency and reproducibility of diagnoses.

1. ARTIFICIAL
INTELLIGENCE IN
PATHOLOGY
PRESENTOR : DR. SAIAKSHAY
MODERATOR : DR. K K SURESH

2. CONTENTS
• INTRODUCTION
• DIGITAL PATHOLOGY
• ARTIFICIAL INTELLIGENCE (AI)
• ARTIFICIAL INTELLIGENCE IN PATHOLOGY
• APPLICATION OF AI IN PATHOLOGY
• LIMITATIONS
• CONCLUSION
• FUTURE PROSPECTS
• REFERENCES

3. INTRODUCTION
• Automation in Pathology are being currently employed in automated
tissue processing, staining and generation of reports
• It progressed to introduction of digital pathology which are now under
process of approval for primary diagnosis
• Digital Pathology(DP) allows remote diagnostic reporting with flexible
hours, ensures service delivery even in challenging situations such as
COVID-19 pandemics and facilitates second and expert opinion
reporting

4. • DP enables implementation of Artificial intelligence based algorithms
in routine practice
• The integration of AI will be the milestone for healthcare in the next
decade

5. Digital pathology
• It is the practice of pathology using digital imaging for clinical and
nonclinical purpose
• A computer based environment using Digital Pathology System as
opposed to a microscope based one.
• It involves scanning and converting of glass slides into high
resolution digital images.
• Digital slides can then be viewed, analyzed, shared, interpreted and
stored using digital pathology system.

6. Digital Pathology Ecosystem consist of 3 major components
1. Information system – access to hospital information system,
electronic medical record, laboratory information system and
radiology picture and communication system (PACS)
2. Digital pathology system – 2 connected subsystem – device to
acquire and manage digital image(WSS) and workstation to view
or share images
3. System tools – to analyse and manipulate the digital images eg:
image analysis algorithm

8. Telepathology
• Telepathology technologies allow light microscopic examination and
diagnosis from a distance.
• These systems may be combined with (Digital learning) DL
algorithms to assist the pathologist to screen a remote case for
consultation or extract features from these images for assisted
diagnosis.
• Telepathology use cases include primary diagnosis, teleconsultation,
intraoperative consultation, telecytology, tele microbiology, quality
assurance,and tele education

9. • Dynamic systems are remote-controlled microscopes that provide the
pathologist with a “live” view of the distant microscopic image while
allowing them to control the stage, focus, and change the
magnification, remotely
TELEPATHOLOGY
DYNAMIC STATIC HYBRID

10. • The second method of telepathology is “store-and-forward”
telepathology
• In which digital images are first captured via cameras or acquired via
WSI systems for transmission to the consultant
• These images can be captured in standard file formats and can be
viewed via a remote viewer.

11. • The third method of telepathology is the use of hybrid systems
• Which can combine features of both “store-and-forward” and dynamic
systems
• Hybrid telepathology systems can provide a “live” view using
robotic-controlled microscopy with high-resolution still image capture
and retrieval. This dual functionality makes them valuable
• Some of the hybrid systems in the market today have capabilities for
WSI as well

12. Digital images
• DPI - composed of pixels arranged in rows and column
• Source - autopsy, grossing room, cameras mounted on microscopes or
scanners, static images, live streaming and most recently whole slide
imaging

13. WHOLE SLIDE IMAGING
• @Virtual microscopy
• Process of generating whole slide image using whole slide
scanner
• A whole slide scanner is an automated robotic microscope
capable of digitizing glass slides
• Consist of microscope, robotics, hardware
and software that scan/ digitalize glass slide
to generate whole slide image

14. • Basic features of WSS - automated barcode reading,
automated tissue identification, autofocus,
autoscanning and automated image compression
• Whole slide scanners use digital cameras with sensors
to capture each FOV using tile-or line-based scanning
methods
• Final whole slide image is produced by stitching of
each FOV which represent a high resolution replica of
the original glass slide

15. • In special conditions(cytology/blood smears), Z-stacking done.
• Quality of images are affected by pre-imaging steps (tissue prep,
staining), various acquisition & processing parameters (lens
objective numerical aperture, digital camera sensor, compression)
and display feature (monitor resolution)
• Image viewing softwares used to microscopic view of the digital
slides with image navigation, panning and zooming movements

16. • In addition, annotations, taking measurements, screenshots, rotate
images, display multiple images side by side, run image analysis
algorithms can be done
• In WSI, lower resolution planes are stacked above higher resolution
regions in a pyramidal architecture so that during retrival, WSI uses
multi-resolution plane to load a small region instead of the entire
image
• Examples of WSI viewers - OpenSlide (open-source viewer),
ImageScope and Webscope (Leica), Sedeen (Pathcore), and IntelliSite
(Philips)

17. Uses of digital images
• Telepathology for consultation
• Multidisciplinary meeting (tumor boards), conferences, presentation
• Documentation, pathology reports
• Archiving
• Quality assurance
• Primary pathology diagnosis
• Remote intra-operative consultation
• Nonclinical – research, education, legal situations

18. Advantage of Digital images
over conventional slides
• Easy portability
• A tremendous advantage in digital pathology is quick access to archived slides
of one patient for comparison of older slides to the actual slides and also to get
quicker access to second opinions worldwide
• Virtual immunohistochemistry service provided by large national laboratories.

19. • WSI images provide very rich and complex information about tissue or
disease characteristics
• It provides platform for development of automated or semi-automated
image analysis methods, aiming to increase the diagnostic accuracy,
reliability, reproducibility and efficiency by enabling quantitative image
analysis
• This leads to open doors for application of Artificial
Intelligence in Pathology

20. What is artificial intelligence
• The term “Artificial Intelligence” is often used to describe the ability of a
computer to perform what appears to be human level cognition
• Rather than thinking, it utilizes information to improve its performance
through learning

21. Are AI, Machine learning, Deep
learning same?
• AI is a branch of computer science about systems which can learn from data
Example:-
 Google predictive searches
Product recommendations on amazon
Music recommendations by Spotify
Google maps ‘ fastest route’
• Machine learning is a branch of AI based on the idea that computer can learn
from data, as humans learn from experience and make decisions about new
data, without human interactions
• Deep learning in turn is a subset of machine learning that uses artificial
neural networks to classify information similar to human brain

23. Deep learning
• ML - the programmer determines the feature vectors (based on which
detection happen) and define it through a process called as feature
engineering
• Neural network in DL can abstract out their own features(representations)
from the inputted data all by itself – feature learning
• These representation are formed in the layer between input and output
called as hidden layer
• Thus neural network learns rules by creating hidden variables from data

24. Artificial neural network
-model within deep learning
Model for deep learning are (very) loosely based
upon the neural network architecture of the
mamalian brain ( visual cortex)

26. Types of Deep learning
Mainly five types of deep/artificial neural networks currently used for
pathological image analysis:
1) CNNs,
2) FCNs,
3) GANs - have recently elicited increasing interest.
4) SAEs
5) RNNs
dominant models used in digital pathology
adopted in some literature but are not the major
methods for pathological image computing

27. • COMPUTER VISION
 A field of computer science trying to mimick human vision.
Deep learning algorithms, like CNN’s perform well in computer
vision tasks
• GRAPHIC PROCESSING UNIT(GPU)
 The chip on the computer’s graphics card designed to rapidly process
images, typically found in powerful gaming computers.
 Indispensable for fast processing of WSI.
• PATCHING
 The size of whole slide images is huge (+/-15GB=3*3h long HD
movies on Netflix, they need to be divided in to smaller parts –
patches . This process is called patching.

28. • DATAAUGMENTATION
 A way to get more input data for training our model when we don’t
have more data by minimally altering structures of interest .
eg- by rotating or flipping mitotic figures.

29. Application of AI in daily life

30. AI in Medicine
• AI can mine electronic health records, journal and textbook materials, patient
clinical support systems for epidemiologic patterns, risk factors and
diagnostic predictions
• Signal classification, such as interpretation of ECG and EEG recordings
• Image analysis in Radiology

31. • Studies on Deep learning shows
Dermatology – detects SCC vs Benign seborrhoeic keratosis with
diagnostic accuracy comparabe with dermatologist
Used in detecting sight threatening retinal diseases using optical coherance
tomography
US FDA approved a deep learning-based autonomous AI diagnostic system
to detect diabetic retinopathy on the images of RETINAL FUNDUS

32. WHY AI IN PATHOLOGY?
• Precision medicine demands precision diagnostics
• The pathologist’s diagnosis based on histological slides is at the
centre of clinical management especially decisions on cancer care
• This emphasizes the need of accuracy in histopathological
diagnosis for personalized therapy

33. • Discordance among pathologist is common as seen in assessing
certain subjective features like cytonuclear pleomorphism/degree
of atypia, cellularity, counting mitotic figures, scoring of tumour
infiltrating lymphocytes and assessment of proliferation (Ki67) index.
• The end point of traditional microscopic slide evaluation is qualitative
assessment of changes in cell number, morphology, distribution
• AI is being anticipated to help pathologists in transition from
providing just traditional qualitative (descriptive) reports to more
quantitative results.

34. • Due to rapid increase in population and decrease in pathology
workforce with even more shortage in sub specialities, there is
requirement of automation to ensure smooth workflow
• With development of AI applications in pathology, they can play a role
of digital assitant to the pathologist

35. Image Analysis
• Image analysis is the extraction of meaningful information from
images
• Specialized image analysis software platforms are commercially
available (e.g., Visiopharm, Indica Labs, Genie from Leica,
Definiens, Virtuoso from Roche)

36. Training set presented to program to differentiate
between 2 entities
Program starts making guess
Guesses are refined through multiple known examples
Generation of internal models to finally make
the right guess
Tested on test set/ validation set before using this
model on unknown unlabelled date

37. • Software algorithms have been developed to identify rare events
(e.g., screening for microorganisms, counting mitoses, and detecting
micro metastases in lymph nodes) and quantify stains (e.g., most
commonly breast biomarkers) and various features (e.g., extent of
tissue fibrosis and degree of liver steatosis).
• They can also analyze spatial patterns and feature distribution better
than humans.

38. • Image algorithms include several steps such as image preprocessing
(e.g., color normalization) following which DL technologies can be
applied to perform
 Classification,
 Segmentation,
 Image translation,
 image style transfer.

39. Classification
• Refers to the task of assigning a label to an image.
• Automatic classification of tissue structures and subtypes can also be
extremely useful to augment and improve the histopathology
workflow
• The leading algorithms for image classification are convolutional
neural networks (CNNs),

40. Segmentation
• Refers to the task of assigning labels to specific regions of an image- can be
seen as a pixel-level classification task.
• Segmentation of subcellular structures can be useful for automating common
tasks - cell enumeration (via nuclei counting), determination of intracellular
locations of molecular markers, analysis of subcellular morphological features
such as nuclear size, eccentricity and chromatin texture
• The most common deep learning architecture for segmentation is U-nets

41. • Multilabel classification is based on image segmentation into regions
of interest

43. picture
Mitotic figure detection

44. Application of segmentation
• Detecting mitoses is a difficult problem because of inter and intra-
observer variability
• Breast tumors are good examples of cases in which proliferation
has prognostic significance
• In 2013, the Assessment of Mitosis Algorithms (AMIDA13) Study
was launched and two-step object detection approach used (first
step identified candidate objects; classified in the second step as
mitoses or non-mitoses)

45. Image Translation
• Refers to the conversion of one image representation to another image
representation such as converting between night & day images, winter &
summer images, satellite images to maps.
• In computational pathology, it has been explored for stain color normalization
and for converting between different image modalities
• The most common models used for image translation tasks are generative
adversarial networks(GAN) or their variants

46. Style Transfer
• Style transfer focuses on transferring the style of a single reference image to
the content of the input image in order to generate an output image
• It is also used for stain-color normalization - input image converted to have
the color gamut and structural color assignments that mimic a target,
reference image

47. Image enhancement
• Image analysis methods find it difficult to cope with variability between
individual stained whole-slide (WSI) images due to different scanners,
staining procedures, and institutional environments
• Algorithms intended to reduce variability between and within microscopy
image datasets developed, collectively designated as stain color
normalization methods
• It includes color deconvolution, clustering based algorithm and recently,
deep leaning algorithm

48. Stain color normalisation

49. Mode switching
• Novel image modalities can be difficult to interpret.
Quantitative phase imaging - lower phototoxicity and portability
Confocal laser endomicroscopy(CLE) - allows for in vivo real-time analysis
of tissue
• Deep neural networks can be used to convert these images to H&E-
like images for better interpretation.
• A GAN was trained to convert the QPI(Quantitative phase imaging)
images to digital H&E stained images.
• Neural style transfer was used to digitally stain CLE images to H&E

50. .

51. In silico labeling
• The process in which stain-free brightfield images reconstructed to
molecularly stained images, highlighting fluorescently labelled
cellular structures
• Rana et al used cGANs to computationally stain images of
unstained paraffin-embedded WSIs.

53. Extended depth-of-field
• High-quality WSIs needs high-numerical aperture lenses to capture
high-resolution images but with restricted fields of view, increasing the
scanning time, as well as shallow depths of field
• An example of the use of deep learning to increase depth of field is an
implementation of a CNN to greatly increase the speed at which
automated scanning and diagnosis of malaria could be made

54. DENOISING
• Another contribution of AI that can markedly improve image quality is reduction
of noise.
• Images with low signals can be difficult to interpret esp. in vivo imaging
challenging
• U-net architecture was successfully utilized for image denoising of
fluorescent microscopy images

56. APPROACHES TO RAPID
HISTOLOGY
INTERPRETATIONS
• Histological data is at the center of decision-making in tumor surgery
esp. in brain
• Relies on frozen section evaluation but time(30-45 min from time of
reception) and labor intensive to prepare the histological slide and
logistics barrier incase of off-site lab

57. • Slide-free histology have been proposed and validated to varying
degrees.
• The most promising tools for imaging unprocessed or minimally
processed biological specimens
• Microscopy with ultraviolet surface excitation(MUSE),
• Stimulated Raman histology (SRH)
• Lightsheet microscopy
• Only SRH has been incorporated into an AI-based diagnostic pipeline
validated for brain tumor diagnosis in a clinical setting.

59. • Hollon et al. adapted the Inception-V2 CNN to diagnose the 10 most
common brain tumors using SRH images and deployed their software
on a bedside SRH imager. In a head-to-head clinical trial of 278
patients,
• Study by Hollon et al. concluded that autonomous diagnostic pathway
utilizing SRH and a CNN could predict diagnosis which was non-inferior to
board-certified neuropathologists interpreting conventional histological slides
in the same patients
• SRH and CNN pathway can be used as a standalone method for brain tumor
diagnosis when pathology resources are unavailable and also to improve
diagnostic accuracy in neuropathology, even in well-staffed centers

60. Application of AI in pathology
Finding tumour deposits within lymph nodes – as a screening method
to reduce the work-load of pathologists
Detection and grading of cancer – whether the tissue studied harbour
tumor or not, if present to do tumour typing, Grading
Gleason grading have been achieved in prostate cancer
method to automatically detect mitotic figures in breast cancer
tissue sections

61. Support and improve the quantification, especially in
immunohistochemistry (stain intensity), resulting in accurate
measurement, therby reproducibility
Measurements of tumour sizes or distances to resection margins
will be more objective compared to classical microscopy.
Pre-check of Papanicolaou stained gynaecological cytology in
cervical cancer screening

62. Enables to create smooth workflow
prioritization of caseloads and workload balancing
to reallocate cases across a network - enables the experts doing
expert work and generalists doing general pathology

63. Text Feature Extraction - connected to pathology-reports, structure
or extract specific text parts from routine pathology reports for
further scientific purposes.
 Text Interpretation and Coding Error Prevention - preventive error
correction, For example, instead of well-differentiated, invasive
breast carcinoma (NST), G2, it should immediately highlight that
“well differentiated” and G2 is incompatible

64. Limitations Of AI
• Heavily data dependant - Training an algorithm requires huge dataset often
numbering in thousands of example unlike human brain
• Training large AI models produces carbon emissions nearly five times
greater than the lifecycle carbon emissions of an automobile
• Image complexity - i.e., the large number of features needed to define the
samples (instances) within the dataset

65. • Current algorithms are trained to perform a specific task with
limited capability of applying the same to new problem
• The detection of implicit bias is made difficult in that we have no
true understanding of how that network is processing its inputs to
arrive at the correct outputs. In other words, there are problems
with explainability and interpretability.

66. For the development of AI in pathology, there must be a close
interaction between the pathologists (as content providers, content
annotators, and ground truth arbiters for training) and the computer
scientists who devise the appropriate machine learning algorithms for
use

67. Conclusion
• Human pathologists will not be replaced by computers
• By removing the mysticism surrounding AI, it becomes clear that it is
a silicon-based digital assistant for the clinical and the research
laboratories rather than an artificial brain that will replace the
pathologist
• Integration of machine learning into routine care will be a milestone
for the healthcare sector in the next decade

68. • Machine learning facilitated the transition from image analysis to
image interpretation.
• AI is also called the third revolution in pathology, after the invention
of immunohistochemistry and next-generation sequencing

69. • Deep learning is capable of
a) Feature engineering
b) Feature learning
• Commonly used artificial neural networks are
CNN and GAN

70. • Predominantely used Artificial neural network in
• Classification -
• Segmentation-
• Image translation -
• In Silico labelling -
Classification – CNN
Segmentation – U-net
Image translation – GAN
In Silico labelling - cGAN

71. • Study by Hollon et al conluded combination of what is comparable
with diagnosis of experts
Stimulated Raman histology (SRH) and CNN

72. Future prospects
The entire field of histopathology is experiencing rapid developments
across all domains.
Opportunities and challenges include:
(a) The wider deployment of conventional whole-slide scanning
capabilities that can finally enable the entry of slide-interpretation into
the digital realm;
(b) The use of AI to enhance the performance of slide-scanning by
interposing color normalization, denoising, sharpening, and depth-of-field
enhancements with
specialized AI tools prior to classification steps;

73. (c) The use of AI to dramatically simplify optomechanical
requirements for scanners
• by correcting for lower raw scan performance with computational
tools, thus decreasing instrumentation costs, scan times, and
inadequate scan performance issues;

74. • (d) Combining novel slide-free imaging hardware and AI to maximize
both information content and similarities with conventional H&E-
stained slide appearance
(e) Getting pathologists, surgeons, administrators, the FDA, and payors
comfortable with the above.

75. These are exciting times, as pathology finally seems poised to take advantage
of notable advances in microscopy and computation.

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