Day Two

Thursday 18th April 2024

8:30 am Networking & Light Breakfast

8:50 am Chair’s Opening Remarks

Aligning Digital Pathology & Omics to Overcome Slow Adoption for Real- World Applications

9:00 am Panel Discussion: Emerging Frontiers: The Intersection of Digital Pathology & Omics to Pioneer Unprecedented Diagnostic Capabilities


  • Exploring the synergy between digital pathology and omics in understanding diseases at a molecular level
  • Discussing how integrating genomic data with digital pathology images enhances treatment decisions
  • Addressing challenges and ethical considerations in managing large-scale omic and pathology dataset

10:00 am Overcoming Real-World Obstacles to Scaling AI & Digital Pathology


  • Slow adoption despite promise
  • Navigating regulatory requirements (CLIA and FDA)
  • Promoting health equity and mitigating biases
  • Generalizability gaps of AI
  • Human-centered design considerations

10:30 am Morning Networking Break

Unveiling the Power of Digital Pathology to Expedite Preclinical Research for Improved Safety & Efficacy of Novel Drugs

11:30 am Understanding the Adoption, Utility & Need for Digitalization in Toxicologic Pathology & Nonclinical Safety Assessment

  • Jimmy Tran Associate Scientific Director - Pathology Nonclinical Safety, Biogen


  • Function and role of toxicologic pathologists in the drug development process, specifically in safety assessment and GLP environments
  • Current uses of digital pathology in toxicology pathology, barriers to increased adoption, and strategies to increase adoption
  • Near-future digital pathology tools that will improve workflows and aid decision-making

12:00 pm High-Throughput Analysis in Preclinical Digital Pathology: Accelerating Research & Discovery


  • Explore how high-throughput digital pathology platforms facilitate rapid and comprehensive analysis of tissue samples in preclinical studies
  • Discuss the impact of high-throughput techniques on data acquisition, processing, and interpretation in preclinical research
  • Harnessing digital pathology for quantification via tissue sample image analysis

12:30 pm Overcoming Challenges in Preclinical Toxicologic Pathology


  • Understanding and overcoming challenges in investigative toxicology
  • Adapting analytical tools for effective safety outcome assessment in diverse animal models

1:00 pm Lunch Break & Networking

Leveraging AI-Enabled Digital Pathology to Accurately Stratify & Select Patients for Improved Drug Response Rate

2:00 pm Harnessing Deep Learning Models in Digital Pathology for Patient Stratification in Immunotherapy

  • Meijian Guan Associate Director - Translational Data Science, Genmab


  • How can computational analysis of pathologic data translate to accurately stratified patients?
  • Integrating multi-omics to determine precision therapies
  • Exploring the challenges regarding implementing AI for stratification

2:30 pm Reproducible and Quality Data for Harnessing AI-enhanced Digital Pathology to Develop Companion Diagnostics to Enhance Drug Response Rate

  • Edwin Roger Parra Cuentas Director of the Multiplex Immunofluorescence & Image Analysis Laboratory, MD Anderson Cancer Center


  • How is AI harnessing pathological data for the identification of novel biomarkers to accurately predict drug response rate?
  • How can different algorithms permit accurate patient selection based on pathological data to determine responders versus non-responders?
  • How can multiple data sources be accurately validated to ensure accurate Companion Diagnostics generation?

3:00 pm Afternoon Break & Networking

Unveiling the Path to Better Pathological Data Handling to Improve Image Quality Whilst Minimizing Cost & Variabilities

3:30 pm Delving into the Essential Validation Steps to Uphold Exceptional Image Quality for Improved Pathological Reliability

  • Sarah Vargas Image Analysis & Electron Microscopy Scientist, Pfizer


  • What is needed to ensure scanner technology is validated for high-quality and consistent imaging?
  • Ensuring consistent validation of tissue samples and slides staining quality to maintain high accuracy rates
  • Considerations for training and evaluating the performance of quantitative image analysis algorithms

4:00 pm Unsupervised Learning Approach for Detailed Cell Type Mapping in H&EStained Images


  • Generating datasets that accurately and exhaustively delineate cells in H&E images is challenging and time-consuming, particularly for achieving comprehensive coverage of the entire cellulome
  • We propose a machine learning framework that utilizes state-of-the-art cell segmentation models in conjunction with a modified self-supervised learning model to capture intricate embeddings of individual cells in H&E whole slide images
  • Our framework is designed to cluster similar cell types, enabling their differentiation in an automated manner. This paves the way for comprehensive, automated classification of cell types in H&E-stained images

4:30 pm Chairs Closing Remarks

4:45 pm End of Translational Digital Pathology & AI Summit 2024