8TH DIGITAL PATHOLOGY & AI
CONGRESS ASIA 2024
10 - 11 September 2024

Lee Kong Chian School of Medicine NTU Singapore (Novena Campus)
The 8th Digital Pathology and AI Congress Asia brings together the pioneers at the point where digitalpathology and artificial intelligence converge. This edition will be co-hosted with Bioinformatics Institute(BII), a cornerstone in bioinformatics under the umbrella of the Biomedical Research Council (BMRC) of
A*STAR, has consistently championed computational biology and bioinformatics-driven research andinnovation.
This edition will be co-hosted with Bioinformatics Institute (BII). Read the open letter, Embarking on the
Era of Digital Pathology and AI: Unveiling the Future of AI Digital Pathology, from BII here.

CO-HOSTPLATINUM SPONSORSSILVER SPONSORSEXHIBITORSMEDIA PARTNERSORGANISING COMMITTEEYU Weimiao

TAN Soo Yong

LIM Tony Kiat Hon

LIANG LiLab Head,
Computational
DigitalPathology
Lab,
Bioinformatics
Institute (BII)

SeniorConsultant
and Head,
National
University
Hospital (NUH)

Chairman &SeniorConsultant,Singapore
GeneralHospital (SGH)

Chief Physician
and Head,
Nanfang
Hospital,Southern
Medical
University

PATHOLOGY COMMITTEETAN Soo Yong

LIM Tony Kiat
Hon

SeniorChairman &Consultant
Seniorand Head,
Consultant,National
Singapore
University
GeneralHospital (NUH) Hospital (SGH)

LEE Hwee Kuan

XU JunVice Dean
Schoolof Artificial
Intelligence,
Nanjing
Universityof Information
Science andTechnology(NUIST)ZHANG Jing

LIANG LiCHEN Wanyuan

Associate
Director,
Changzheng
Hospital,
Naval
Medical
University(NMU)

Chief Physician
and Head,
Nanfang
Hospital,Southern
Medical
University

Director
Assistant,Hangzhou
MedicalCollege

LI Zaibo

Associate
Director,
The Ohio
State
University

Head of
Cellular
Image
Informatics
Division,
Bioinformatics
Institute (BII)

Lewis
HASSELL
Professor of
Excellence in
Anatomic
Pathology,
Pathology The
University ofOklahoma

ARTIFICIAL INTELLIGENCE COMMITTEEYU Weimiao

XU JunLab Head,
Computational
DigitalPathology
Lab,
Bioinformatics
Institute (BII)

Vice Dean Schoolof Artificial
Intelligence,
Nanjing Universityof Information
Science andTechnology (NUIST)CHENG Chee
Leong
Head and Senior
Consultant,Singapore
GeneralHospital (SGH)

LEE Hwee Kuan
Head of Cellular
Image
Informatics
Division,
Bioinformatics
Institute (BII)

MattiasRANTALAINEN
Associate Professor& Founder,
Karolinska Institutet& Stratipath)8TH DIGITAL PATHOLOGY & AI CONGRESS ASIA 2024
10 - 11 September 2024
Lee Kong Chian School of Medicine NTU Singapore (Novena Campus)

KEYNOTE SPEAKERS
Alexander "Sasha" SICULAR, Executive Lead, Healthcare and Life Sciences (Formerly Head of Digital Pathology),
Google Public Sector, USA
Richard CHEN, Doctor, Harvard University, USA (NATURE MEDICINE)CAO Gang, Professor, The Brain Cognition and Brain Disease Institute of Shenzhen Institute of Advanced
Technology, Chinese Academy of Sciences, China (NATURE BIOMEDICAL ENGINEERING)
Hoifung POON, General Manager, Microsoft Health Futures, USA (NATURE)

INVITED SPEAKERS
LIU Yueping, Professor & Director of Pathology Department, The Fourth Hospital of Hebei Medical University,
China
ZHANG Jing, Professor & Vice President of the First Affiliated Hospital (YuHang), Zhejiang University, China
WANG Hao, Professor, National Institutes for Food and Drug Control, China (IEEE STANDARD)ZHANG Guanglei, Associate Professor, Beihang University, China
Aileen WEE, Emeritus Consultant Pathologist & Emeritus Professor, National University Hospital & National
University of Singapore, Singapore
WANG Liansheng, Assistant Professor, Xiamen University, China
David ZHANG, Professor of Pathology & Oncological Sciences, State University of New York Downstate Medical
Center, USA
Giovanni LUJAN, Director of Digital and Computational Pathology, Ohio State University, USA
Gareth TURNER, Consultant Histopathologist & Honorary Senior Clinical Lecturer, Oxford University Hospitals
NHS Foundation Trust, UK
TAN Char Loo, Senior Consultant, National University Health System, Singapore
CHEN Hao, Assistant Professor, The Hong Kong University of Science and Technology, Hong Kong
WANG Xiangxue, Professor, Nanjing University of Information Science & Technology, China
KHOR Li Yan, Senior Consultant, Singapore General Hospital, Singapore
Arvind RAO, Associate Professor of Computational Medicine and Bioinformatics, University of Michigan, Ann
Arbor, USA
Lewis HASSELL, Professor of Excellence in Anatomic Pathology, Director of Anatomic Pathology, The Universityof Oklahoma, USA
Mohammad Faizal AHMAD FAUZI, Professor, Multimedia University, Malaysia
Marwan CHAMI, Subject Matter Expert Digital Pathology, Merck Healthcare KGaA, Darmstadt, Germany
Supriya SRIVASTAVA, Senior Research Fellow, National University of Singapore, Singapore
Peter SCHUEFFLER, Professor, Technical University of Munich, Germany
Lee A.D. COOPER, Associate Professor of Pathology, Director, Division of Computational Pathology, Director,
Center for Computational Imaging and Signal Analytics, Northwestern University Feinberg School of Medicine,USA
Evan KELLER, Richard and Susan Rogel Professor of Oncology & Director of the Single Cell Spatial Analysis
Program, University of Michigan, USA
XU Chuan, Professor, Jinfeng Laboratory, China
Desiree ABDURRACHIM, Principal Scientist, MSD, Singapore
Naoko TSUYAMA, Project Scientist & Pathologist, Japanese Foundation for Cancer Research, Japan
Ahmed FADIEL, Computational Oncology Unit Director & Professor, University of Chicago, USA
LI Zaibo, Professor of Pathology and Endowed University Pathology Service Professorship, Director of
Cytopathology and Cytopathology Fellowship, Associate Director of Digital and Computational Pathology, The
Ohio State University, USA
Bing REN, Assistant Professor, Dartmouth Hitchcock Medical Center and Geisel School of Medicine, USA
Xinmi HUO, Senior Research Officer, Agency of Science, Technology and Research, Singapore

CONGRESS SCHEDULE

10th September 2024

08:50

Global Engage Welcome Remarks & Chairpersons' Opening Remarks

09:00

KEYNOTE ADDRESS
Alexander "Sasha" SICULAR, Executive Lead, Healthcare and Life Sciences (Formerly Head of Digital
Pathology), Google Public Sector, USA
EXPLORING THE ECONOMICS OF DIGITAL PATHOLOGY FOR THE BROADER INDUSTRY AND YOUROWN IMPLEMENTATION
Continued technical advancements in Digital Pathology have not gone unnoticed by decision makers
around the world. Yet, has the industry advanced enough to make now the right time for an
implementation within your own organization? Each organization will need to reach a decision on theclinical, research and operational merits that digitization can bring to their practice. Assuming you
are ready to move forward, how do you start calculating the costs - and benefits? This talk will
explore various technical input costs to weigh against the benefits when making these decisions.
While every organization will have its own needs, this conversation will offer an economic framework
to consider as you explore your options.

09:30

KEYNOTE ADDRESSCAO Gang, Principal Investigator, The Brain Cognition and Brain Disease Institute of Shenzhen
Institute of Advanced Technology, Chinese Academy of Sciences, China
SINGLE-CELL SPATIAL MULTI-OMICS AND NEXT GENERATION PATHOLOGY
Multicellular organisms elegantly orchestrate the co-transcription of the right genes in a precise
spatial-temporal manner. Delineation of the comprehensive spatial-omics will pave the way tounderstand the molecular function of specific cells in sophisticated organs and is also crucial for next
generation pathology. Recently, we developed a high throughput Multi-omics in situ Pair-wise
Sequencing (MiP-Seq) strategy to efficiently decipher multiplexed DNA, RNA and Protein in intact
tissue at subcellular resolution. This method can differentiate SNP and RNA m6A modification, andwas applied to detect tumour gene mutation and allele specific expression of parental genes. MiPSeq can be combined with in vivo Ca2+ imaging to obtain spatial multi-omics atlas correlated to theneuronal activity. This method contributes to spatial multi-omics and precise diagnosis.

10:00

Epredia Speaking Session
Topic: TBC
Abstract: TBC

10:30

Morning Refreshments/ Poster Presentation/ One-to-One Meetings
Implementation, Strategy & Technology &Application

11:30

WANG Hao, Professor, National Institutes for
Food and Drug Control, China
TREND OF INTERNATIONAL STANDARDIZATIONOF
AI-ENABLED
MEDICALDEVICEFOR
PATHOLOGICAL APPLICATION

Computational Pathology & AI

Peter
SCHUEFFLER,
Pathology, Technical
GermanyProf.
Computational
University of Munich,

COMPUTER AIDED PATHOLOGY: ON THE
CHALLENGES AND CHANCES OF IN A ROUTINELABCONGRESS SCHEDULE

11:55

10th September 2024

In this presentation, the progress of current
international standardization of AI-enabled
medical device is discussed. The topics include
general testing and evaluation, specific testing
methods. data quality control and terminology.
The impact of such standards on AI pathologicalapplications is further analysed, which may help
develop specific standards for AI-enabled
pathological image processing software.

Transition to a digital workflow in a routine
diagnostic lab is challenging due to high costs
and extensive change management for both thelab personnel and the medical doctors.
However,thetransition
opens
new
opportunities
andefficiency
gains
for
pathologists: From modern and family-friendly
work
environments
tonew
AI-assisted
diagnostic tools. We present the lessons learned
after going fully digital in our pathology lab
together with early results of multimodal AI for
Glioma subtyping built with the help of digitalpathology.

Lee A.D. COOPER, Associate Professor of
Pathology Director, Division of Computational
Pathology; Director, Center for Computational
Imaging and Signal Analytics, Northwestern
University Feinberg School of Medicine, USA

Gareth David Huw TURNER, Consultant andHonorary
SeniorLecturerin
Haematopathology,
OxfordUniversity
Hospitals NHS Trust, UK

TRANSPARENT PROGNOSTIC MODELING OF
INVASIVE BREAST CANCER OUTCOMES
Breast cancer is a heterogeneous disease withvariable survival outcomes. Pathologists grade themicroscopic appearance of breast tissue using theNottingham criteria, which are qualitative and do
not account for noncancerous elements within thetumor microenvironment. This talk will present theHistomic
Prognostic
Signature(HiPS),
acomprehensive, interpretable scoring of thesurvivalrisk
incurredbybreasttumor
microenvironment morphology. HiPS uses deeplearning to accurately map cellular and tissue
structures to measure epithelial, stromal, immune,and spatial interaction features. It was developed
using a population-level cohort from the Cancer
Prevention Study-II and validated using data fromthree independent cohorts, including theProstate, Lung, Colorectal, and Ovarian Cancer
trial, Cancer Prevention Study-3, and The Cancer
Genome Atlas. HiPS consistently outperformed
pathologists in predicting survival outcomes,
independent of tumor-node-metastasis stage andpertinent variables. This was largely driven bystromal and immune features. In conclusion, HiPS
is a robustly validated biomarker to support
pathologists and improve patient prognosis.

APPLYING AI TO THE DIAGNOSIS OF
MYELOPROLIFERATIVEDISEASESIN
HAEMATOPATHOLOGY
Using machine learning on digital pathology
images from bone marrow trephines of patientswith
Myeloproliferative
diseases(MPNs),
quantitative description of megakaryocyte
morphology and reticulin fibrosis identify
distinct megakaryocyte phenotypes with clinicalutility in distinguishing MPN from reactive
samples and associating with underlying driver
mutations (1). Automated analysis andcontinuous indexing of fibrosis (CIF) captures
heterogeneity within MPN marrows allowing
refined classification and disease monitoring.
Combining topological data may help predictpatients at increased risk of progression e.g. toassist in the discrimination of ET and prefibrotic PMF (pre-PMF) (2). The potential of
combining digital imaging for diagnostics andmachine learning approaches has applications
acrosshistopathology
teaching/training,
diagnosis/classification, drug trials and basic
research, combining single cell transcriptomics
for investigation of haematopoietic stem cellbiology and its consequences for MPN patients.

CONGRESS SCHEDULE
12:20

ZHANG Jing, MD, PhD, President & Vice
President of the First Affiliated Hospital(YuHang) of Zhejiang University School of
Medicine, China
PARTNERING WITH AI: HOW PATHOLOGISTSCAN THRIVE IN THE DIGITAL AGE
The emergence of digital pathology and artificial
intelligence (AI) has introduced uncertainty inthe field of pathology. This talk will discuss majoradvances in digital pathology and AI, and explore
their potential impact on the roles ofpathologists. While AI excels at automatingroutine tasks and aiding in analysis, the expertiseof pathologists remains crucial. We will examine
how AI can augment a pathologist's workflow,
improving efficiency and diagnostic accuracy.
The discussion will address concerns about job
displacement and explore the evolving skillset
pathologists will need to thrive in the digital age.
This talk aims to provide a balanced perspectiveon the future of pathology, emphasizing
collaboration between humans and AI to deliver
exceptional patient care.

12:50

Lunch/ Poster Presentation/ One- to- One Meetings

13:50

PANEL DISCUSSIONMAXIMIZING ROI AND WORKFLOW EFFICIENCYTHROUGH AI: STRATEGIES FOR EFFECTIVE
DIGITAL PATHOLOGY IMPLEMENTATIONThis panel will delve into how integrating artificial
intelligence (AI) into digital pathology can
improve diagnostic accuracy and streamline
workflow processes. Experts will share practicalstrategies for implementing AI tools, maximizing
return on investment (ROI), and optimizing
workflow efficiency. Gain valuable insights into
the transformative power of AI in pathology, andlearn how to leverage these technologies toimprove patient care and enhance operational
outcomes.

10th September 2024
LIU Yueping, Professor & Director of Pathology
Department, The Fourth Hospital of Hebei Medical
University, China
AI-ASSISTED ASSESSMENT AFTER NEOADJUVANTTHERAPY FOR BREAST CANCER
Neoadjuvant therapy has become a standard
clinical practice for shrinking tumor size, lowering
tumor stage, increasing surgical opportunities, andimprovingbreastconservation
rates.
Postoperative pathological evaluation of residual
tumors and lymph node metastasis is closely
associated with the prognosis and long-term
survival of patients. This paper focuses on thecurrent challenges and complexities associatedwith evaluating breast cancer after neoadjuvant
therapy. We conducted a comparative analysis of
the merits and drawbacks associated withconventional assessment methods and AI-assisted
assessment, providing comprehensive insightsfrom five key perspectives:
1. AI-assisted assessment of the tumor bed
following neoadjuvant therapy for breast cancer.
2. AI-assisted in identifying cases of lymph nodemetastasis after neoadjuvant treatment for breast
cancer.
3. AI-assisted assessment of alterations in tumor
infiltrating lymphocytes and immune-related
biomarkers following neoadjuvant therapy forbreast cancer.
4. AI-assisted prediction of treatment response
and prognosis after neoadjuvant therapy forbreast cancer.
5. Future prospects for further development.

CHEN Hao, Assistant Professor, The Hong Kong
University of Science and Technology, Hong Kong
TOWARDS MULTIMODAL DATA INTEGRATIONFOR ADVANCING PRECISION ONCOLOGY
Artificial intelligence (AI), especially deep learningwithlarge-scaletraining
datasets,
has
dramaticallyadvancedtherecognition
performance in many domains including speech
recognition, visual computing and natural
language processing.

CONGRESS SCHEDULE

14:15

10th September 2024

Discussion Points include:
How can AI integration enhance diagnostic
accuracy in digital pathology, and what are
the key considerations for successful
implementation?
What strategies can healthcare organizations
adopt to maximize ROI when implementing AI
tools in digital pathology?
How does AI impact workflow efficiency indigital pathology labs, and what are the bestpractices for optimizing workflow processes?
What are the current challenges andlimitations of AI in digital pathology, and how
can these be addressed to improve adoption
and effectiveness?
How can healthcare professionals ensure thatAI technologies in digital pathology comply
with regulatory requirements and ethicalstandards?

Despite its breakthroughs in above domains, its
application to precision oncology remains yet to
be explored, where large-scale fully and highquality annotated datasets are not easily
accessible. In this talk, I will share our recent
progress on computational pathology withmultimodal data integration for precisiononcology through the lens of information theory,
including cross-modal consistency, multimodalalignment and knowledge decomposition, withversatile applications to disease diagnosis,
treatment response prediction, prognosis, etc.
Challenges and future directions will also be
discussed.

Panelists:
Lewis HASSELL, Professor of Excellence in
Anatomic Pathology, Director of Anatomic
Pathology, The University of Oklahoma, USA
LI Zaibo, MD PhD MBA, Professor of Pathology
and Endowed University Pathology Service
Professorship, Director of Cytopathology andCytopathology Fellowship, Associate Directorof Digital and Computational Pathology, The
Ohio State University, USA
Ahmed FADIEL, Computational Oncology Unit
Director & Professor, University of Chicago,
USA
Bing REN, MD PhD, Assistant Professor,
Dartmouth Hitchcock Medical Center andGeisel School of Medicine at Dartmouth, USA

ARTIFICIAL INTELLIGENCE IN PROSTATE CANCERPATHOLOGY: HYPE OR TODAY’S REALITY
With the wide availability of digital pathology,
artificial intelligence (AI) for pathology applications
has drawn significant attention and promises great
potential in pathology practice. We have developeda robust and flexible image management system(IMS) that allows integrating digital images into
pathology practice.
Furthermore, we developed an AI platform toenhance the ability of pathologists to review anddiagnosis prostate cancer in biopsy sections. Our
study demonstrated that AI can improve accuracy ofidentifying malignant glands, especially small glands.
The objectives are:David Y. ZHANG, Adjunct Professor, State Universityof New York Downstate Medical Center, USA

1. Review the current development of AI applicationsin prostate pathology.
2. Present our IMS and AI in prostate biopsy
applications.

14:40

Medmain Speaking Session

Solution Provider Presentation
For sponsorship opportunities please contact
Reuben Raj:
reuben@global-engage.com /+603 2779 0098
ext 1005

CONGRESS SCHEDULE
14:55

10th September 2024

Early Career Researcher Presentation
TAN Char Loo, Senior Consultant, National
University Health System, Singapore

Early Career Researcher Presentation
Supriya SRIVASTAVA, Senior Research Fellow,
National University of Singapore, Singapore

INTEGRATING DIGITAL PATHOLOGY INTO DAILYOPERATIONS: REALITIES AND CHALLENGES
Digital pathology represents a transformative
leap forward in the field of diagnostic medicine,
revolutionizing the way pathologists examine
tissue samples and make critical clinicalassessments. By harnessing cutting-edge imaging
technology
andadvancedcomputational
analysis, digital pathology enables the digitizationof traditional glass slides, allowing for highresolution viewing and analysis of tissue samples
on computer screens. This innovative approach
not only enhances the efficiency and accuracy ofpathological evaluations but also facilitates
remote collaboration, education, and research inways previously unattainable with conventional
microscopy. As healthcare systems worldwide
embrace digitalization, digital pathology emerges
as a cornerstone of modern pathology practice,promising to redefine standards of care,accelerate diagnostic workflows, and unlock newfrontiers in precision medicine. Hereby, I will be
sharing our experiences and the challenges we
faced in integrating digital pathology into thedepartment’s daily operation.

SPATIALLY RESOLVED NICHE AND TUMORMICROENVIRONMENTAL
ALTERATIONSINGASTRIC CANCER PERITONEAL METASTASES
Peritoneal metastases (PM) in gastric cancer (GC)
portend a poor prognosis, yet our understandingof tumor microenvironmental (TME) characteristics
associated with GCPM remain limited. In this study,
we sought to understand TME features that
determine and facilitate peritoneal organotropism
from primary GCs. We conducted digital spatial
profiling (DSP) on independent tumor and stroma
compartments (DSP, GeoMx platform, Nanostring
Technologies, Inc) of 55 GC patients with PM.
Slides were stained for PanCK (epithelial cells),
CD45 (immune cells) and SMA (stroma), andregions of interest (ROIs) were selected based onthese markers. A total of 712 ROIs were retrieved
after quality control evaluation. Unsupervised
clustering of the ROIs revealed a clear distinction
between adjacent normal and tumor, and between
the tumor and stroma compartments. UMAP
analysis demonstrated close clustering andoverlap between the PT and PM for tumor ROIs,
suggesting transcriptomic similarity.

15:10

Afternoon Break/ Poster Presentation/ One- to- One Meetings

16:10

Desiree ABDURRACHIM, Principal Scientist,
MSD, Singapore
MULTI-MODAL AI DIGITAL PATHOLOGY TO
ACCELERATE MASH DRUG DISCOVERY
Spatial biology is critical in the study of theliver because of the metabolic zonation in theliver lobules. We leverage AI digital pathology
toinvestigatechanges
in
the
liver
microenvironment during the progression of
Metabolic Associated Steatohepatitis (MASH).

Ahmed FADIEL, Director and Co-Founder of the
University of Chicago Medicine, Computational
Oncology Unit, University of Chicago, USA
A BIOINFORMATICS APPROACH TO DIGITALPATHOLOGY
Digital pathology revolutionizes diagnostics withhigh-resolution whole slide imaging (WSI) but
necessitates robust data management solutions.
Bioinformatics platforms leverage cloud storage
and distributed computing for efficient WSI
storage, retrieval, and analysis.

CONGRESS SCHEDULE

16:25

10th September 2024

In this talk, I will provide an overview of ourmulti-modal AI digital pathology toolboxes,
which include Second Harmonic Generation(SHG),
traditional
H&E/Masson’s
Trichrome/PSR, dual IHC, multiplexed-IF, andintegrated spatial omics imaging. I will also
highlight how we utilize the technology tounderstand human disease biology, andthrough reverse translation, how we guideanimal model selection, proof-of-biology, andproof-of-concept, to accelerate our MASH drug
discovery pipeline. Beyond discovery efforts, I
will also touch upon the industry’s effort inleveraging AI digital pathology to aid
pathologists in MASH scoring and its potential
to improve the efficiency of MASH our clinical
trials.

Machine learning algorithms, specifically deep
convolutional neural networks (CNNs), automate
image analysis tasks like tumor classification andsegmentation, achieving superior accuracy andconsistency compared to manual analysis.
Standardization protocols like DICOM and OME
ensure seamless data exchange across disparate
platforms, fostering collaboration. Rigorous
validation techniques employed by bioinformatics,
including statistical analysis and concordancestudies, guarantee the reliability and clinicaladoption of these algorithms. Secure dataencryption and access control mechanisms
safeguard patient privacy and HIPAA compliance.
By
integrating
bioinformatics
with
digitalpathology, we unlock its potential to transformpatient care, enabling the development of
personalized treatment plans and advancing
precision medicine initiatives.

Mohammad Faizal AHMAD FAUZI, Professor,
Multimedia University, Malaysia

Bing REN, MD PhD, Assistant Professor,
Dartmouth Hitchcock Medical Center and Geisel
School of Medicine at Dartmouth, USA

REAL-TIMEANALYSISOFHORMONE
RECEPTORS FOR BREAST CANCER TREATMENTRECOMMENDATION
The talk will present a real-time system for the
analysis of breast cancer hormone receptors
from glass slides, useful in recommending
effective treatments for breast cancer patients.
The approach is based on automatic detection,
segmentation and classification of individual
cells
in
estrogen
receptor(ER)
immunohistochemistry (IHC) image streams,
before Allred scoring is carried out as perstandard clinical practice. For real-time
implementation, an industrial machine vision
camera is mounted to the microscope, streaming
the image from the microscope glass slide for
digital analysis. The proposed system and setup
provide a great platform to incorporate artificial
intelligenceonconventional
glass-based
diagnosis and prognosis. This will be especially
useful in countries lacking DP facilities andpolicy.

MACHINE LEARNING IN GASTROINTESTINAL
AND LIVER PATHOLOGY
Machine learning (ML) significantly impacts
gastrointestinal (GI) pathology. In our institution,
ML is applied to both luminal GI and liver
pathology. Several deep learning systems havebeen developed to diagnose celiac disease withan accuracy of 95%, classify four most common
colorectal polyps (tubular adenoma, villous
adenoma, hyperplastic polyp, and sessile serrated
adenoma) with an accuracy of 87%, anddifferentiate esophageal tissue as either normal,
nondysplastic Barrett’s, dysplastic Barrett’s, or
adenocarcinoma with an accuracy of 85%. We
also apply ML and co-expression analyses to help
identify biomarkers from Digital Spatial Profilerassayed protein expression patterns inside andaway from the tumor, which allows theidentification of spatial proteomic signatures ofcolon tumor metastasis. In addition, we
conducted a large-scale validation study of theviability of generative adversarial networks(GANs) for H&E to trichrome conversion, which
demonstrates that virtual trichrome technologies
may offer a software solution for the diagnosis ofliver fibrosis.

CONGRESS SCHEDULE
16:50

Giovanni M LUJAN, MD, Associate Professor,
Directorof
DigitalandComputational
Pathology, The Ohio State University, USA
EMBRACING THE DIGITAL FRONTIER: THE OHIOSTATE UNIVERSITY’S TRANSFORMATION INTOA FULLY DIGITAL ACADEMIC HUB
The Ohio State University (OSU, Department of
Pathology in the United States stands at theforefront of academic innovation as the firstlarge-scale academic institution in the USA totransition entirely into a Digital Workflow.This
pioneering journey reflects a commitment toembracing technology to enhance patient care,educational delivery, research capabilities, andalso administrative efficiency. The digitaltransformation at OSU is redefining the healthcare and educational landscape, offering
seamless access to cutting edge tools to assist in
our role as diagnosticians as well as
collaborative research and learning platforms.
This presentation outlines the key strategies,
challenges, and triumphs of OSU’s digitaljourney, highlighting our current status and alook into the foreseeable future.

17:15

10th September 2024
Marwan CHAMI, Subject Matter Expert Digital
Pathology, Merck Healthcare KGaA, Darmstadt,
Germany

A DEEP LEARNING IMAGE ANALYSIS WORKFLOW
FOR ANTIBODY DRUG CONJUGATE (ADC)
TARGETS
Tumor cell (TC) membrane markers are
frequently used as predictive biomarkers inselection of cancer patients for drugs directed atmembrane bound targets, like antibody-drug
conjugates (ADCs). However, scoring of TC
membrane biomarkers can be challenging,leading to inconsistency, and limiting the value as
predictive biomarker. Hence, the need for a morerobust, reliable, and quantitative membrane
detection approach. Here, we report on our deeplearning approaches with the goal to provide
advanced quantification of TC membrane-based
immunohistochemistry (IHC) biomarkers.

Evan KELLER, Richard and Susan Rogel Professor of Oncology & Director of the Single Cell Spatial
Analysis Program, University of Michigan, USA
SINGLE CELL AND SPATIAL ANALYSIS OF UROLOGIC CANCERS
The recent explosion of high plex methods to evaluate biomolecules at the single cell level in situ has
opened the door to identifying novel interactions between tumor cells and their microenvironment,
spatial relationships between cell types and signaling processes, and potential biomarkers for prognosis
and predictiveness. Using these methods, we have (1) identified spatiotemporal changes in tissue
structure, cell composition and signaling associated with androgen deprivation in a murine model; (2)determined putative biomarkers in prostate cancer biopsies that may improve accuracy of tumor
grading; and (3) delineated a mechanism through which renal clear cell carcinoma progresses to a renal
sarcomatoid cancer subtype. These studies demonstrate how single cell and spatial analytic methods
can inform both mechanistic and translational studies.

17:40

End of Day 1/Networking Drinks Reception

CONGRESS SCHEDULE

11th September 2024

08:50

Global Engage Welcome Remarks & Chairpersons' Opening Remarks

09:00

KEYNOTE ADDRESS
Richard CHEN, Doctor, Harvard University, USA
A GENERAL-PURPOSE SELF-SUPERVISED MODEL FOR COMPUTATIONAL PATHOLOGY
Quantitative evaluation of tissue images is crucial for computational pathology (CPath) tasks,
requiring the objective characterization of histopathological entities from whole-slide images (WSIs).
The high resolution of WSIs and the variability of morphological features present significant
challenges, complicating the large-scale annotation of data for high-performance applications. To
address this challenge, current efforts have proposed the use of pretrained image encoders throughtransfer learning from natural image datasets or self-supervised learning on publicly available
histopathology datasets, but have not been extensively developed and evaluated across diverse
tissue types at scale. We introduce UNI, a general-purpose self-supervised model for pathology,
pretrained using more than 100 million images from over 100,000 diagnostic H&E-stained WSIs (>77
TB of data) across 20 major tissue types. The model was evaluated on 34 representative CPath tasksof varying diagnostic difficulty. In addition to outperforming previous state-of-the-art models, wedemonstrate new modeling capabilities in CPath such as resolution-agnostic tissue classification,slide classification using few-shot class prototypes, and disease subtyping generalization inclassifying up to 108 cancer types in the OncoTree classification system. UNI advances unsupervised
representation learning at scale in CPath in terms of both pretraining data and downstream
evaluation, enabling data-efficient artificial intelligence models that can generalize and transfer to awide range of diagnostically challenging tasks and clinical workflows in anatomic pathology.

09:30

KEYNOTE ADDRESS
Hoifung POON, General Manager, Microsoft Health Futures, USA
MULTIMODAL GENERATIVE AI FOR PRECISION HEALTH
The dream of precision health is to develop a data-driven, continuous learning system where newhealth information is instantly incorporated to optimize care delivery and accelerate biomedical
discovery. The confluence of technological advances and social policies has led to rapid digitizationof multimodal, longitudinal patient journeys, such as electronic medical records (EMRs), imaging, andmultiomics. In this talk, I'll present our research progress on multimodal generative AI for precision
health, where we leverage real-world data to pretrain powerful multimodal patient embedding, which
can serve as digital twins for patients. This enables us to assimilate multimodal, longitudinal
information for millions of cancer patients, and apply the population-scale real-world evidence toadvancing precision oncology in deep partnerships with real-world stakeholders.

10:00

Leica Speaking Session
Topic: TBC
Abstract: TBC

10:30

Morning Refreshments/ Poster Presentation/ One-to-One Meetings /Group Photo Session

Implementation, Strategy & Technology &Application

AI for Imaging and Digital Image Analysis

CONGRESS SCHEDULE
11:30

ArvindRAO,
Associate
ProfessorofComputational Medicine and Bioinformatics,
University of Michigan Ann Arbor, USA
MACHINE LEARNING APPROACHES TO THE
INTERPRETATION
OF
THE
TUMR
MICROENVIRONMENTUSINGSPATIALIMMUNO-PROFILING&SPATIAL
TRANSCRIPTOMICS,FORPERSONALIZEDMEDICINE
Spatial profiling technologies like hyper-plex
immunostaining in tissue, spatial transcriptomics
etc have the potential to enable a multi-factorial,
multi-modal characterization of the tissue
microenvironment.
Scalable,quantitative
methods to analyze and interpret spatial
patterns of protein staining and gene expression
are required to understand cell-cell relationships
in the context of local variations in tissue
structure. Objective scoring methods inspired byrecent advances in statistics and machinelearning can serve to aid the interpretation ofthese datasets, as well as their integration withother, companion data like genomics. In this talk,we will discuss elements of spatial profiling frommultiple studies as well as paradigms fromstatistics and machine learning in the context ofthese problems. This talk will also discuss the useof AI/ML and spatial analytics of the tumor
microenvironment to derive spatial biomarkers
of immunotherapy.

11:55

WANG Xiangxue, Professor, Nanjing Universityof Information Science and Technology, China
IMAGE BASED BIOMARKER FOR CANCERPROGNOSIS AND TREATMENT RESPONSE
PREDICTION
The spatial arrangement of cells within tissues
and tumors can provide valuable information forpredicting cancer prognosis and treatment
response. The diagnosis and prognosis predictionof cancer typically rely on pathological analysis
and biochemical examinations of the diseased
tissue. However, these methods overlook theheterogeneity of cancer cells within tumors andthe complex interactions between different cell
types. Spatial arrangement and structural
analysis, such as spatial transcriptomics andimaging techniques, offer new tools for11th September 2024
ROUNDTABLE DISCUSSION TOPICS
Table 1DATA QUALITY AND AVAILABILITY IN DIGITALPATHOLOGY: STRATEGIES FOR ENSURING
ACCURACY AND ACCESSIBILITY
Discuss the strategies for ensuring the accuracy
and accessibility of data in digital pathology,
including quality control measures, data
management
practices,andcollaborative
initiatives to improve data sharing andavailability.
Moderator: Lee A.D. COOPER, Associate Professorof Pathology, Director, Division of Computational
Pathology, Director, Center for Computational
Imaging and Signal Analytics, Northwestern
University Feinberg School of Medicine, USA
Table 2EXPLORING THE POTENTIAL OF LLM AND
GENERATIVE AI IN PATHOLOGY
Discuss the potential of large language models(LLMs) and regenerative artificial intelligence (AI)in pathology, including their applications in image
analysis,
diagnosis
support,anddata
interpretation. Explore how these technologies
can enhance pathology practice by improving
accuracy,
efficiency,
anddecision-making
processes.
Moderator: Bing REN, MD PhD, AssistantProfessor, Dartmouth Hitchcock Medical Center
and Geisel School of Medicine at Dartmouth, USA
Table 3CANCER CARE: THE ROLE OF DIGITALPATHOLOGY, AI, AND WSI IN ADVANCINGRESEARCH, TREATMENT, AND DIAGNOSTICPRECISION
Examinehow
digitalpathology,
artificial
intelligence (AI), and whole slide imaging (WSI) are
revolutionizing cancer care. Discuss their impact
on research, treatment, and diagnostic precision,
highlighting advances in image analysis, molecularprofiling, and personalized medicine.
Moderator: Lewis HASSELL, Professor of
Excellence in Anatomic Pathology, Director of
Anatomic Pathology, The University of Oklahoma,
USACONGRESS SCHEDULE

12:20

researchers to analyze the spatial locations of cells
within tissues or tumors, thereby deepening ourunderstanding of the tumor microenvironment and theinteractions between cancer cells and neighboring
cells. With advancements in computational algorithms
and artificial intelligence, mining the spatial
relationships of cells within tumors from biomedical
data and establishing their associations with cancer
patient prognosis can offer new approaches and meansfor precise treatment of cancer patients. The speakerhas been engaged in the analysis of pathologicalimages and the fusion of multimodal medical
information, and has made preliminary explorations in
the prognosis and treatment prediction of malignant
tumors such as lung cancer.

Table 4NAVIGATINGDIGITALPATHOLOGYREGULATIONS AND COMPLIANCE FORQUALITY ASSURANCE
Discuss the regulatory landscape of digitalpathology, including key regulations andstandards that govern its implementation.
Explore strategies for ensuring compliance,
including quality assurance measures,
documentation requirements, and staff
training.

EARLY CAREER RESEARCHER
Xinmi HUO, Senior Research Officer, Computational
Digital Pathology Lab (CDPL), Bioinformatics Institute(BII), Agency of Science, Technology and Research,
SingaporeEARLY CAREER RESEARCHERLAU Mai Chan, Assistant PI; Head ofComputational
Immunology
Platform;
Adjunct Assistant Professor, Bioinformatics
Institute(BII),A*STAR;
Singapore
Immunology Network (SIgN), A*STAR; Lee
Kong Chian School of Medicine, NTU,
Singapore

A COMPREHENSIVE AI MODEL DEVELOPMENT
FRAMEWORK FOR DIGITAL PATHOLOGY: USINGGLEASON GRADING IN PROSTATE CANCER AS AN
EXAMPLEThis presentation introduces a generalized framework
designed for the development of AI-based digitalpathology solutions, with prostate cancer (PCa)
Gleason grading serving as a specific application
showcase. The framework addresses prevalent
challenges such as inconsistent image quality, ongoingdata integration, and scanner variability. It integrates
A!MagQC for image quality control, A!HistoClouds forcloud-based annotations, and employs a Pathologist-AI
Interaction (PAI) for continual model enhancement.
Initially trained on Akoya-scanned images using
advanced generalization techniques,
this model
demonstrates strong generalization across various
scanners. Comprehensive testing on whole slide
images from five different scanners showed significant
improvements: the detection of Gleason patterns
achieved an increase in F1 scores from 0.73 to 0.88,
and the model reduced Gleason scoring time by 43%.
The adaptability of this framework not only enhances
diagnostic accuracy and efficiency in prostate cancer
grading but is also applicable to other areas ofpathology.
12:35

11th September 2024

Poster Competition Winner Presentation
Poster competition abstract submission deadline
is 19th August 2024.

GENERATIVE AI FOR ROBUST SPATIALBIOMARKER DEVELOPMENT
Our research addresses key challenges suchas the lack of effective biomarkers and thelimited understanding of the tumor
microenvironment. In this talk, I will discuss
how advanced spatial omics techniques
have enhanced our understanding of thiscomplex environment. Our preliminary
analysis demonstrates the potential of
multi-omics
biomarkers
comparedtoconventional approaches. I will also share
our recently published work on using virtual
staining to make spatial omics data moreaccessible from clinically available histology
H&E slides. Finally, I will showcase our webbased visualization tool, which enhances
both our virtual staining methods and multiomics
analysis
of
the
tumor
microenvironment.

Poster Competition Winner Presentation
Poster competition abstract submission
deadline is 19th August 2024.

CONGRESS SCHEDULE

11th September 2024

12:50

Lunch/ Poster Presentation/ One- to- One Meetings

13:50

KHOR Li Yan, Senior Consultant, Singapore
General Hospital, Singapore

WANG Liansheng , Professor, Xiamen University,
China

JOINING THE AI FRAY – CHALLENGES AND
REWARDSFROMAHISTOPATHOLOGIST’SPERSPECTIVE
Having collaborated with AI scientists fromgovernment agencies, research institutes andcommercial vendors to develop and validating AI
algorithms for histopathology, this talk will discussthe:• outline the unexpected challenges and rewards• provide insights from my varied experience
For pathologists who are interested in getting theirfeet wet in algorithm development, this talk will
include lessons learnt on providing high quality DP
image data for algorithm development, theprocess of transferring mutual domain knowledge(between pathologist and AI scientist) relevant to
the project, encountering limitations on DP image
formats and SDKs, providing high quality
annotations
inaninternet-separated
environment, time-boxing of project goals for
efficient results and considerations for the retraining of an algorithm with new data.

PATHOLOGICALIMAGEANALYSIS:FROMMICROSCOPIC TO ULTRA-MICROSCOPIC
This presentation explores the frontier ofpathological image analysis. We delve into theadvancements in deep learning that have
revolutionized the analysis of pathologicalimages, enabling not only the identification of
diseases at earlier stages but also the predictionof disease progression and response totreatment. Our approach harnesses the power ofcomputational analysis to interpret complex
patterns within pathology images and multiplex
data sets. We highlight several applications wherethese techniques have been successfully applied,
including tumor classification, grading, and themapping of genetic aberrations. Furthermore, thepresentation will address the challenges ofintegrating pathology data with other multiplex
data sources, such as genomic and proteomic
data, to achieve a comprehensive understandingof disease mechanisms. Through case studies andrecent research findings, we aim to demonstrate
how the synergy between deep learning andultra-microscopic image analysis is paving theway for a new era in precision medicine anddiagnostic pathology.

XU Chuan, Professor, Jinfeng Laboratory, China

ZHANG Guanglei, Associate Professor, Beihang
University, China

14:05

THE
PRACTICALIMPLEMENTATIONOFARTIFICIAL INTELLIGENCE ASSISTED DIGITALPATHOLOGY IN CLINICAL AND TRANSLATIONALRESEARCHESThe detection of progression and recurrence atearly stage from molecular pathology perspectiveis essential for companion diagnostics inanticancer therapy. Artificial intelligence (AI)assisted digital pathology with ultrasensitive
detecting capacities which can identify biomarkers
presented in low abundance may serve as
potential instruments.

EXPLICITLY BRIDGE PATHOLOGICAL AND
NATURAL IMAGE WITH PUZZLESPathological image analysis is a crucial field incomputer vision. Due to the annotation scarcity
in the pathological field, most of recent works
have leveraged self-supervised learning (SSL)
trained on unlabeled pathological images, hoping
to mine the representation effectively. However,there are two core defects in current SSL-based
pathological pre-training: (1) they do not explicitly
explore the essential focuses of the pathologicalfield, and (2) they do not effectively bridge withand thus take advantage of the knowledge fromnatural images.

CONGRESS SCHEDULE

14:30

Herein, we demonstrated the application of AIdriven assay in discovering novel targets andpathological features of cancer patients related
to clinical outcomes and their underlying
mechanisms in immune escape and treatment
tolerance with examples from recent finding
from our lab.

To explicitly address them, we propose ourPuzzleTuning framework. irstly, we identify threetask focuses that can effectively bridge knowledgeof pathological and natural domain. Secondly, we
devise a novel multiple puzzle restoring task,
explicitly pre-training the model. Thirdly, we
introduce an explicit prompt-tuning process toincrementally
integratethedomain-specificknowledge, aligning the domain gap between naturaland pathological images. Experimental results
verified our PuzzleTuning framework.

Aileen WEE, Emeritus Consultant Pathologist &Emeritus Professor, National University
Hospital & National University of Singapore,
Singapore

Naoko TSUYAMA, Project Scientist & Pathologist,
Division of Pathology, Cancer Institute, JapaneseFoundation for Cancer Research, Japan

YOUR PATH WITH AI: CAN AI-ASSISTIVE TOOLSLEAD THE WAY FORWARD IN HISTOLOGICALASSESSMENT OF LIVER FIBROSIS IN MASH?
Artificial intelligence (AI) and digitised wholeslide images (WSI) have enabled the use of AIassistive tools in Pathology to improve
histopathological interpretation. Evaluation ofliver
fibrosisinmetabolicdysfunctionassociated steatohepatitis (MASH) is importantin both clinical practice and interventional trials.
AI-assistive tools such as qFibrosis have beenshown to improve pathologist agreement for
fibrosis scores in MASH patients. There is also
potential for digital pathology with AI (DP/AI)
analyses in overcoming current limitations ofconventional static ordinal scoring systems,
such as assessing treatment-induced changes
like fibrosis regression. DP/AI tools which
provide a quantitative, continuous evaluation offibrosis have the capability to capture subtle
changes in fibrosis that are more reflective ofthe dynamic nature of fibrosis. The potential andshortcomings of AI-assistive tools for liver
fibrosis assessment in MASH are discussed.

14:55

11th September 2024

End of Day 2/ Conference Ends

INTEGRATING GROSS SPECIMEN PHOTOS AND WSI
FOR
ENHANCED
DIGITAL
MAPPINGOFHISTOLOGICAL REGIONS
Integrating pathology data is essential in addressing
complex diagnostics, the increasing number of
specimens, and the pathologist shortage. The adventof digital pathology platforms has heightened thefocus on the integrated analysis of whole-slide
images (WSIs) alongside data from varied modalities,
such as gross photo images paired with WSIs.
Pathologists initially perform a visual inspection of
specimens to decide which areas require sectioning
for histological examination. These sections are thenprocessed into glass slides and converted into WSIs.
Traditionally, pathologists assess these images
separately. However, recognizing that both image
sets are derived from identical specimen sections
but exhibit different modalities is critical.
Correlating macroscopic and microscopic images isvital for digitally mapping histological regions totheir corresponding specimen surfaces. This
presentation will explore our department's daily
diagnostic routine for this procedure and highlightthe benefits of this integrated approach forenhancing
diagnostic
accuracy
anddatamanagement efficiency.

CONGRESS SCHEDULE

11th September 2024

POSTER PRESENTATIONThis conference definitely provides an ideal platform to showcase your works with a captive audience. Whether
looking for funding, job opportunities or simply wanting to share your work with a like-minded and focused group,
poster presentations are an excellent way to join the heart of the congress.
Poster presentation abstract submission deadline is 19th August 2024.

POSTER COMPETITION
We are pleased to announce that we will be hosting a competition as part of this conference.
Poster Competition Requirements:
Submission of poster form
Submission of poster in pdf form
Submission deadline: 19th August 2024
Two exceptional entries will be selected to receive the following rewards:$1000 Travel Grant
15-Minute Speaking Position on the Conference Program
Certificate of Participation
Certificate of Achievement – Poster Competition
Representatives from solution provider organisations or experts already speaking on the program are not eligible to enter the competition
but are welcome to present posters at the meeting as normal.

Download poster presentation/competition abstract form HERE.
Contact: haley@global-engage.com for any inquiries