This document provides an outline and introduction to a presentation on artificial intelligence in pathology. It discusses digital pathology, artificial intelligence, and how AI is being applied in pathology. Key applications of AI mentioned include image analysis through deep learning algorithms for tasks like classification, segmentation, and detecting rare events. Limitations of AI are also noted. The integration of AI has the potential to help pathologists provide more quantitative analysis and improve workflows.

1. ARTIFICIAL INTELLIGENCE
IN PATHOLOGY
PRESENTOR : DR. K.SIVARANJINI
MODERATOR : DR. ATHER FATHIMA (PROFESSOR)
DR. ZAHEDA KAUSHER (ASSOCIATE PROFESSOR)
DR. SHRUTHI (ASSISTANT PROFESSOR)

2. OUTLINE
• INTRODUCTION
• DIGITAL PATHOLOGY
• ARTIFICIAL INTELLIGENCE (AI)
• ARTIFICIAL INTELLIGENCE IN PATHOLOGY
• APPLICATION OF AI IN PATHOLOGY
• LIMITATION
• CONCLUSION
• REFERENCE

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
• 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. 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

9. 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

10. • 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

11. • 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)
• Imaging viewing softwares used to microscopic view of
the digital slides with image navigation, panning and
zooming movements

12. • In addition, annotations, take 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)

13. 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

14. 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.

15. • 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

16. 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
• Asking if a machine can think is like asking whether a
submarine can swim
• Rather than thinking, it utilizes information to improve
its performance through learning

17. ARE AI, MACHINE LEARNING, DEEP
LEARNING SAME?
• AI is an umbrella term enclosing the methods for a
computer to emulate, or exceed in some extent, human
intelligence
• Machine learning is a part of AI that equips systems with
the ability to learn from experience without being
explicitly programmed to do so
• Deep learning in turn is a subset of machine learning that
uses artificial neural networks to classify information
similar to human brain

19. 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

20. ARTIFICIAL NEURAL NETWORK
Model for deep learning are (very) loosely based
upon the neural network archietecture of the
mamalian brain ( visual cortex)

21. 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

22. APPLICATION OF AI IN DAILY LIFE

23. 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 EKG and
EEG recordings
• Image analysis in Radiology

24. • Studies on Deep learning shows
Dermatology – detects SCC vs Benign seborrhoeic
keartoses 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

25. 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

26. • Discordance among pathologist is common as seen in
assessing certain subjective features like cytonuclear
pleomorphism/degree of atypia, cellularity and 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.

27. • Due to rapid increase in population and decrease in
pathology workforce with even more shortage in
subspecialities, 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

28. 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)

29. 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

30. • 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.

31. • 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.

32. 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),

33. 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

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

36. 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)

37. 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

38. 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

39. 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

40. STAIN COLOR NORMALISATION

41. MODE SWITCHING
• Novel image modalities can be difficult to interpret.
Quantitative phase imaging - lower phototoxicity
and portability
Confocal laser endomicroscopy - 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 images to digital
H&E stained images.
• Neural style transfer was used to digitally stain CLE
images to H&E

43. 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.

45. 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

46. 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

47. RAPID HISTOLOGY INTERPRETATION
• 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

48. • 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
• SRH has been incorporated into an AI-based diagnostic
pipeline validated for brain tumor diagnosis in a clinical
setting.

50. • 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

51. 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

52. 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

53. 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

54. 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

55. 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

56. • 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.

57. 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

58. 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 routinee care
will be a milestone for the healthcare sector in the
next decade

59. • 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

60. REFERENCES
• Bancroft’s Theory and Practical Of Histological
Techniques – Eight Edition
• Artificial intelligence and Deep Learning In Pathology
• Artificial Intelligence in Medicine
• Encyclopedia of Biomedical Engineering,Volume2

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

62. • 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

63. • Study by Hollon et al conluded combination of what is
comparable with diagnosis of experts
Stimulated Raman histology (SRH) and CNN
• Which tumors are graded using AI algorithm
Breast and Prostate