Pathology News

How Close Are We to Personalized Treatment Decisions with Causal Machine Learning?
2026-06-22 14:00 - 15:00 (Europe/London) | Online
0000Days00Hrs00Min00SecThe Tissue Image Analytics (TIA) Centre hosts a seminar series for those interested in computational pathology. The series invites researchers and leaders in computational pathology and related fields to present their work and foster thought-provoking discussions.
This seminar features Octavia Ciora and Maresa Schröder from LMU Munich, Munich, Germany.
Abstract
Understanding the efficacy and safety of treatments is crucial for optimizing patient outcomes. However, patients respond differently to the same treatment due to biological, clinical, or contextual factors, which highlights the need for personalized clinical evidence to guide treatment decisions. Causal machine learning offers a promising way to do so by combining causal inference principles with flexible ML models to estimate personalized treatment effects. In this talk, we present findings from a scoping review of 70 clinical studies applying causal machine learning in personalized medicine. We assessed 41 key methodological choices, including clinical context, model, validation, heterogeneity analysis, and clinical translation. We show how these causal ML is currently used, how their results are validated, and where important gaps remain. Finally, we propose a validation framework with methodological recommendations to promote reliable clinical evidence generation.
Register NowBio
Octavia Ciora is a second-year PhD student in computer science at LMU Munich, supervised by Prof. Stefan Feuerriegel. She holds bachelor’s and master’s degrees in bioinformatics from the Technical University of Munich and LMU Munich. Her research lies at the intersection of causal machine learning and healthcare. Her work focuses on developing machine learning-based frameworks for clinical decision-support, treatment effect estimation from electronic health records, and personalized treatment recommendations for oncology. Maresa Schröder is a final-year PhD student in computer science at LMU Munich, supervised by Prof. Stefan Feuerriegel. She holds a mathematics master’s degree from the Technical University of Munich and bachelor’s degree in economics and mathematics from the University of Mannheim. Her research interest lies in causal machine learning with a special focus on reliable treatment effect estimation, such as the development of uncertainty quantification and robust estimation methods while ensuring ethical constraints. Furthermore, she is interested in developing advanced causal methods for medical applications and strategic decision-making.

From Images to Insights: Getting Started with RNAscope™ Image Analysis in HALO®
2026-06-25 16:00 - 17:00 UTC
0000Days00Hrs00Min00SecThis webinar will start at 8:00, PDT, 11:00 EDT, 16:00 BST
Discover commonly used tools to get started today with RNAscope™ image analysis in HALO®

Summary

Join us for this 1-hour webinar to learn about the latest RNAscope™ assays from Bio-Techne Spatial and to see a live demonstration of RNAscope™ image analysis using the HALO® platform from Indica Labs. This webinar will demonstrate how to effectively leverage HALO’s ISH and FISH modules for RNAscope analysis, as well as how to leverage neural networks in HALO AI to improve cell segmentation and classify tissue regions. In addition, select HALO modules and workflows that are commonly used with RNAscope™ image analysis will be demonstrated. Attendees will leave with practical guidance and best practices for building scalable, reproducible RNAscope analysis workflows in HALO, making this session ideal for researchers and core facilities working with both brightfield and fluorescence assays.

Learning Objectives

Overview of the RNAscope technology, key assays and applications
Learn about HALO modules for brightfield and fluorescent RNAscope™ image analysis
Discover how HALO AI advances RNAscope™ image analysis with superior nuclear segmentation and custom tissue classification
Learn about HALO tools commonly used in RNAscope™ analysis such as the TMA (Tissue Microarray) Module and the Spatial Analysis module

Register Now
Presenters

Spencer Revill, MSc
Field Applications Scientist
Indica Labs
Spencer Revill is a Field Application Scientist at Indica Labs supporting digital pathology and image analysis with a background in biotechnology and medical sciences. Prior to joining Indica, he managed a translational research core facility and conducted graduate research in lung pathology using digital image analysis. Spencer now works closely with customers to support evaluations, proof‑of‑concept studies, and adoption of HALO, HALO AI, and HALO Link across a wide range of research applications. His work focuses on helping researchers translate complex image data into reliable, reproducible insights using digital pathology.
Anushka Dikshit, PhD
Senior Manager, Spatial Biology Applications
Bio-Techne
Anushka is a Senior Manager and group leader for the Applications Marketing Management team at Bio-Techne Spatial with background in cancer research and immuno-oncology. Anushka and her team are focused on expanding the applications of the RNAscope technology across different research areas through internal and external collaborations. Prior to working for Bio-Techne, she was involved in identifying novel therapeutic targets for melanoma as a postdoctoral fellow at Duke University. She obtained her doctoral degree from Southern Illinois University, School of Medicine in Molecular Physiology.

TIA Centre Seminar Series – Pathology Foundation Models for Text-prompted Segmentation and Precision Oncology
2026-06-29 14:00 - 15:00 (Europe/London)
0000Days00Hrs00Min00SecThe Tissue Image Analytics (TIA) Centre hosts a seminar series for those interested in computational pathology. The series invites researchers and leaders in computational pathology and related fields to present their work and foster thought-provoking discussions.
This seminar features Dr Junlin Hou from The Hong Kong University of Science and Technology, Hong Kong.
Abstract
Computational pathology is rapidly moving from task-specific image analysis toward foundation models that enable scalable, generalizable disease diagnosis and clinical decision support. In this talk, I will first present our recent work, PathSegmentor, a foundation model that enables flexible, text-prompted segmentation across diverse pathological entities. Beyond segmentation, I will also provide an overview of our broader efforts in pathology foundation models, including image enhancement, whole-slide representation learning, benchmarking, and agentic AI systems. These works aim to advance computational pathology toward more precise, interpretable, and clinically useful AI for precision oncology.
Register NowBio
Dr. Junlin Hou is a Post-doctoral Fellow in the Department of Computer Science and Engineering at The Hong Kong University of Science and Technology (HKUST). She obtained her Ph.D. from the School of Computer Science at Fudan University in 2023. She has published 30+ papers in top-tiered journals and conferences, including Nature Computational Science, Nature Biomedical Engineering, IEEE TMI, MICCAI, CVPR, etc. She has served as a guest editor for CMIG and J. Imaging, and as a reviewer for 15+ journals and conferences. She also led the team winning 10+ medical grand challenges. Her current research interest is in trustworthy medical AI and computational pathology.

Pathologist and AI: A Collaboration in Practice
2026-06-29 15:00 - 16:00 (Europe/London)
0000Days00Hrs00Min00SecThis webinar will begin at 4:00 pm CEST, 5:00 pm EEST, 10:00 am EDT, 3:00 pm BST
Even the most accurate AI models only fulfill their full potential when they fit seamlessly into a pathologist’s daily practice. That’s why we explore the collaboration of the pathologist and the AI in the upcoming webinar. The conversation around AI in pathology fixates on the algorithm — but the real difference is made in how the pathologist and the AI work together.
During this session, Pathologist and Aiforia’s Medical Director, Richard Doughty, will discuss:

Better together: why the AI–pathologist collaboration matters more than the AI itself
AI and pathologists, side by side — see how it’s done
Real labs, real results — how AI is being used today, with practical examples from real-world sites
A live case, worked in real time

This webinar will give a clear, practical view of what good AI–pathologist collaboration actually looks like.
Register NowSpeakers

Richard Doughty, DVM, MD, MSc, Medical Director, Aiforia Technologies

Consulting pathologist at Oslo University Hospital
Medical doctor and specialist in pathology with extensive diagnostic experience.
Qualified veterinarian with pathology expertise in pharmaceutical research and development.

Emilia Lönnberg, Commercial Product Manager, Aiforia Technologies

Has worked at Aiforia for 4 years
MSc in life science technologies from Aalto University, major in human neuroscience

NPIC Digital Pathology Webinar Series - Innovation into Practice - From Research to Real-world Impact: AI and Innovation at NPIC.
2026-06-30 15:00 - 16:00 (Europe/London)
0000Days00Hrs00Min00SecThe National Pathology Imaging Co-Operative (NPIC) continues an exciting new series of free educational webinars on digital pathology topics.​​

Learn first-hand about hot topics in clinical digital reporting, the digital laboratory, IT and informatics and get a chance to pose your questions to the experts!​
Suitable for pathologists, biomedical scientists, informatics and IT professionals, managers and industry representatives.​

​In this session:

Discover advances in research and innovation in digital pathology and AI.
Find out more about NPIC’s multi-scanner research facility and the AI-FORGE platform.
Gain insights into supporting collaboration, enabling real-world research, and building the foundations for the future of AI-enabled pathology.

Register NowPresenters
Dr Daljeet Bansal
NPIC Operations Director

Dr Matthew Humphries
NPIC Head of Research

Dr Bethany Williams
NPIC Knowledge and Skills Lead

QC at Scale: Moving Beyond Manual Review in Digital Pathology
2026-07-08 09:00 - 10:00 (Europe/London)
0000Days00Hrs00Min00SecThis webinar will start at 10AM EDT, 7AM PDT, 4PM CET, 9am BST
As digital pathology volumes grow, manual quality control is struggling to keep pace. Slide defects like out-of-focus regions and missed tissue can slip through undetected, reaching pathologists’ queues and undermining diagnostic confidence, turnaround times, and AI readiness.
This webinar explores how AI-powered automation is transforming lab QC: catching issues early, reducing rework, and ensuring only the highest-quality images reach your pathologists.
What you’ll learn

Why manual QC breaks down at scale — and the hidden costs when it fails
How automated QA improves speed, consistency, and workflow efficiency across your lab
Why image quality is the foundation of every successful digital pathology and AI strategy

This webinar is ideal for…
Lab managers, QC staff, researchers and anyone involved in managing whole slide imaging workflows and their quality control
Register NowSpeaker
Katherine LatimerProduct Manager Digital PathologyBarco
Kat Latimer holds a Master of Engineering in Biomedical Engineering from Colorado State University and a BS in Biochemistry from the University of New Mexico. Her graduate research focused on tissue engineering and end-to-end medical device development. After spending several years at Indica Labs, she currently leads Barco’s digital pathology product efforts.

NPIC Digital Pathology Webinar Series - Innovation into Practice - NPIC Digital Pathology Platform and Deployment Strategy
2026-07-14 03:00 - 04:00 UTC
0000Days00Hrs00Min00SecThe National Pathology Imaging Co-Operative (NPIC) continues an exciting new series of free educational webinars on digital pathology topics.​

Learn first-hand about hot topics in clinical digital reporting, the digital laboratory, IT and informatics and get a chance to pose your questions to the experts! ​
Suitable for pathologists, biomedical scientists, informatics and IT professionals, managers and industry representatives.

In this session:

Find out about the NPIC technical platform for digital pathology!​
Hear about the opportunities and challenges of digital pathology deployment, and the NPIC approach.​
Pose questions to our speakers in our live Q and A session!

Register NowSpeakers:
Ian Mason
Programme Head for Digital Deployment & Business Services​

Waseem Akhtar
Portfolio Delivery Manager

Dr Bethany Williams
NPIC Knowledge and Skills Lead

TIA Centre Seminar Series – A Multimodal and Temporal Foundation Model for Virtual Patient Representations at Healthcare System Scale
2026-07-27 14:00 - 15:00 (Europe/London)
0000Days00Hrs00Min00SecThe Tissue Image Analytics (TIA) Centre hosts a seminar series for those interested in computational pathology. The series invites researchers and leaders in computational pathology and related fields to present their work and foster thought-provoking discussions.
This seminar features Andrew Zhang from Harvard Medical School, Boston, USA.
Abstract
The field of medical AI has traditionally focused on isolated clinical domains, such as chest X-rays or pathology slides. These data modalities have the advantage of being plentiful, relatively standard in format, and information dense. But by looking at only individual snapshots of a patient’s medical journey, are we missing the forest for the trees? Outside of the world of curated medical imaging datasets, there are petabytes of clinical data existing in the electronic health record (EHR) as unstructured or semi-structured longitudinal events. These include diagnoses, symptoms, lab tests, clinical reports, medications, and more, each tied to a specific timepoint in the course of a patient’s healthcare journey. With our capacity to train increasingly larger and more powerful AI models, the next frontier is to build a virtual patient: a model that understands and makes predictions on the entire EHR. In this talk, I will introduce APOLLO, a major effort towards modeling whole patients: advancing the state of the art not only in data size (25 billion events from 7.2 million patients) but also in data breadth (structured data, clinical text, and images) and downstream capabilities. To evaluate the potential of this model, we created a large-scale benchmark consisting of over 300 patient retrieval and clinical forecasting tasks and showed that APOLLO patient embeddings generalize across medical domains and encode both retrospective and prognostic information about each patient. APOLLO has exciting applications not only in disease risk prediction but also as a multimodal medical search engine and unlocks the possibility of biomarker discovery from the entire EHR.
Register NowBio
Andrew Zhang is a 4th-year PhD student in the Harvard-MIT Health Sciences and Technology Program, working in the lab of Dr. Faisal Mahmood at Harvard Medical School. Prior to his PhD, Andrew studied cell biology and computer science at Harvard. His work centers on advancing self-supervised representation learning at multiple biological and temporal scales: from encoding a single pathology slide to encoding entire longitudinal medical histories.

Meet the Next Generation of Concentriq: Pathology’s AI Platform for Precision Medicine
2026-07-28 17:00 - 18:00 (Europe/London)
0000Days00Hrs00Min00SecThis webinar will start at 12pm EST, 5pm BST, 6pm CET, 9am PST
Diagnostic labs are being asked to do more than ever. Drug development teams are racing to find the right patients for increasingly complex therapies. The old playbook, a pathology platform with AI bolted on the side, can’t keep up.
Proscia built the next generation of Concentriq to be a different kind of platform. Concentriq Fifth Generation introduces intelligence that draws from clinical and scientific context to support experts throughout the decisions that define drug discovery and shape the future of diagnosis.
Join Proscia for the official first look. We’ll demo the platform live, hear from customers on the impact, and walk through how Concentriq helps labs deliver high-volume case reviews and R&D teams advance their programs.
In this session:

How Concentriq Fifth Generation brings intelligence into everyday case review and scientific workflows
What pathologists, lab leaders, and scientists can accomplish that wasn’t possible before
Real-world examples of intelligence accelerating case review, lab operations, biomarker discovery, and clinical development
A live look at Concentriq Fifth Generation in action

Whether you’re a pathologist, scientist, operations leader, or precision medicine leader, this is the session that changes how you think about what your pathology platform can do for you.
Register NowSpeakers
David West
Chief Executive Officer
Proscia
Coleman Stavish
Chief Technology Officer
Proscia
Dr. Cherub Kim
Director, Medical Affairs and Clinical Informatics Lead
Labcorp
Dr. Daniel Roberts
Medical Director of Digital Pathology
Cleveland Clinic
Dr. Amanda Hemmerich
Global Director of Digital Pathology Innovation
IQVIA Laboratories

Sneak Peek Into the New Features of HALO®, HALO AI, and HALO Link 4.4
2026-09-24 16:00 - 17:00 (Europe/London)
0000Days00Hrs00Min00SecThis webinar will start at 8:00 PDT, 11:00 EDT, 16:00 BST
Learn about the exciting new features in the 4.4 versions of HALO, HALO AI, and HALO Link!
Summary
Join us for an exciting webinar highlighting the latest improvements in HALO®, HALO AI, and HALO Link coming in version 4.4.
HALO 4.4 introduces enhanced fluorescent channel management, including channel sorting and grouping as well as new black/white adjustment controls, settings that can be saved and applied across images. The new Object Data Gallery provides high-resolution views of a random sample of objects, making object-level QC faster and easier.
HALO AI 4.4 introduces new AI architectures: UNET++ for advanced masking classifier development and EfficientNet for the next generation of Phenotyper V2. Mixed-precision training and inference reduce GPU memory usage while accelerating model development and execution. Annotation workflows are also improved through a more compact annotation format, reducing database impact and export file sizes. Updated platform libraries further enhance performance, stability, and compatibility.
HALO Link 4.4 delivers improved study tree navigation, a new “Save As” option for Figure Maker, improved Diagnostic Monitor visualization, and other quality-of-life enhancements.
The 4.4 release includes updates to the Multiplex IHC and Highplex FL modules, including adjustable membrane thickness and ability to control post-processing on the module side for embedded AI segmentation modules. Additional enhancements include threshold assistance for brightfield images and new object data outputs for nuclear and cellular morphology, signal OD/intensity heterogeneity, and Haralick texture features. Highplex FL also delivers improved performance when users autofill dyes along with faster addition and removal of dyes, even for images with hundreds of channels.
Learning Objectives

Learn about the improved fluorescent channel management in HALO, including channel sorting and grouping.
Learn about the newly introduced Object Data Gallery feature for easier QC of object results.
Learn about the newly available AI architectures for masking classifiers and Object Phenotyper V2.
Learn about the numerous quality-of-life features in HALO Link.

Register NowPresenter
Dr. Noemi Kedei
Director of Product, Life Sciences
Indica Labs
Noemi Kedei earned an MD degree from University Medical School of Debrecen, Hungary. She did her postdoctoral training and worked later as a staff scientist in the Laboratory of Cancer Biology and Genetics, NCI. Her research focused on identifying mechanisms involved in the differential effects of natural compounds and their engineered derivatives acting on protein kinase Cs (PKCs), validated targets for anti-cancer treatment. In 2018 she joined the Collaborative Protein Technology Resources (OSTR, CCR, NCI) core facility to implement a variety of spatial protein and transcriptomics technologies, and lead related operations as services to NCI investigators. Noemi provided expertise for development of project based customized antibody protein panels for detection of 40-60 protein targets at single cell level in tissue sections using the CODEX/Phenocycler fusion technology, and ensured optimal and efficient instrument use. In addition, she advised and supported users on tools for quantitative image analysis combining different AI-enabled HALO modules and NCI developed pipelines for downstream analysis. Noemi joined Indica Labs as Director of Product – Life Sciences in May, 2025.
