Free, open-source tools for quality control and tissue image analysis—streamline your pathology workflow and reduce batch effects.
This course introduces you to the HistoSuite toolset: a group of free, open-source solutions developed to improve consistency, efficiency, and quality control in digital pathology workflows. Across three recorded webinars, you’ll learn how to identify and manage stain variability, scanning artifacts, and other common challenges in pathology image datasets. This resource is especially useful for professionals who want to reduce annotation overhead, maintain H&E staining consistency, and avoid misleading trends due to batch effects. If you’re tired of manual slide quality checks, this course is your starting point.
Why you CANNOT MISS this HistoQC Course?
Are you tired of sorting through all kinds of stain variations, slide preparation and scanning artifacts manually in your pathology image dataset? Afraid that the trend you identified in your image data is actually a reflection of a batch effect? Or maybe you just want to stop wasting time on useless annotations and know exactly which patches are relevant for your model development? No, not that either? How about keeping you H&E staining consistent in your lab?
If you resonate with any of the above, THIS COURSE is a MUST, because we have FREE tools to solve your problems!
The HISTOSUITE TOOLS.
FREE Tools for Computational Pathology!
HistoBlur
HistoBlur is a deep learning-based tool that allows for the fast and accurate detection of blurry regions in Whole Slide Images.
Quick Annotator
An open-source digital pathology tool for rapidly annotating objects.
HistoQC
Open-source tool for generation of reproducible slide quality metrics with artifact localization.
Cohort Finder
Intelligent data partitioning tool that uses quality control metrics. It helps you identify batch effects, design the ideal model training/ test and validation set and can increase the performance and generalizability of machine learning model.
Patch Sorter
Patch Sorter is an open-source digital pathology tool for histologic object labeling. It helps you annotate fast exactly the right patches to train your model.
What You’ll Learn
#1
How to handle stain variation and image artifacts
#2
Tools for consistent H&E staining and image quality control
#3
What batch effects are and how to minimize them
#4
How to reduce time spent on unnecessary annotations
#5
Free, open-source software to support better pathology workflows
Why Take This Course?
Quality Control Made Simple
Learn how to use tools that highlight inconsistencies and reduce batch effects.
Time-Saving Insights
Focus on what matters by skipping irrelevant annotations.
Free & Open-Source
Use practical tools that require no investment.
Real-World Pathology Challenges
Tackle common problems in slide preparation, staining, and scanning.
By the End You’ll…
#1 Know how to maintain consistency in slide quality and staining
#2 Understand how to spot and reduce batch effects in image data
#3 Be familiar with the HistoSuite tools and their core functionality
#4 Feel empowered to implement better QC strategies in your lab
Course Modules Include
Slide Quality, Batch Effects & Annotation Overload (Webinar Series About HistoSuite – Part 1)
Quick Annotator & Quick Annotator Pro (Webinar Series About HistoSuite – Part 2)
PatchSorter and HistoQC Workflow in Practice (Webinar Series About HistoSuite – Part 3)
About Your Speakers
Aleksandra Zuraw, DVM, Ph
Dr. Zuraw is a veterinary pathologist actively involved in tissue image analysis work since entering the field of digital pathology in 2016.
She is a digital pathologist practicing pathology on digital slides. Because of the impact digital pathology has on patient care and her life, her mission is to PROMOTE DIGITAL PATHOLOGY IN THE SCIENTIFIC COMMUNITY with her blog, podcast and social media presence and bridge the gap between pathology and computer science.
She also likes walking around and taking pictures.
Andrew Janowczyk, PhD
Dr. Andrew Janowczyk’s research focuses on applying computer vision and machine learning algorithms to digital pathology images including algorithms helping with disease detection and cancer grading. His newer research focuses on the tasks of predicting prognosis and therapy response in the oncology domain. His recent ITCR R01grant aims to develop open-source tools to facilitate cancer research through improved quality control, annotation, and identification of slides for sophisticated machine learning algorithms.Headline
He also likes cooking, and mountain stuff.
FREE COURSE about HistoSuite Tools
Stop wasting time and start optimizing your pathology image dataset today! Join our FREE course and gain access to powerful tools that will eliminate the hassle of manual sorting, batch effects, useless annotations, and inconsistent staining. Don't miss out on this opportunity to take your model development to the next level. Enroll now and revolutionize your pathology research!
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