Get the PDF of "Digital Pathology 101" Book here

Image analysis has supported pathology since the introduction of whole slide scanners to the market, and when deep learning entered the scene of computer vision tissue image analysis gained superpowers.

There are regulatory compliant AI-based image analysis tools available for practicing pathology around the globe.

So what shall you do, just embrace them and start using?

I would learn a bit about image analysis and AI first, to be able to make an informed decision.

Good news, you can get all the information needed for this informed decision from this very chapter of the "Digital Pathology 101" book that I have published for you. 

From Chapter 3 you will learn the fundamentals of tissue image analysis and how it helps extract meaningful data from digital pathology images. 

We break it down into basic concepts like

•  regions and objects of interest, 
• matching computer vision techniques to pathology tasks, and the
•  differences between classical machine learning and AI-based deep learning approaches.  

Understanding these foundations sets the stage for appreciating how image analysis is applied in regulated clinical settings versus exploratory research environments. You will  learn the importance of quality control, because flawed data inputs inevitably lead to faulty outputs, regardless of the analysis method used.

Moving on, you will familiarize yourself with the key terminology from the world of artificial intelligence and machine learning. 

The chapter clarifies the meaning of concepts like 

• supervised learning, 
• GPUs, 
• data augmentation, and 
• heat maps. 

It emphasizes how techniques like 

• patching and 
• data augmentation 

enable the training of machine learning algorithms on large datasets.

 
Ultimately, by comprehending this terminology and the basics of tissue image analysis, you'll gain clarity on how these tools can provide decision support to pathologists through computer-aided diagnosis. Rather than seeing AI as a black box, you'll have insight into how it arrives at its outputs. 

With this balanced understanding, you'll be equipped to make discerning choices about embracing AI tools in your pathology practice, leveraging their benefits while being aware of current limitations. 

Stay tuned as we continue unpacking the transformative potential of digital pathology!
Talk to you in chapter 4!

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Get the PDF of "Digital Pathology 101" Book here

Get the paper copy  of "Digital Pathology 101" on AMAZON

Watch the "Digital Pathology 101" Book Launch here

Become a Digital Pathology Trailblazer and See you inside the club: Digital Pathology Club Membership

This is the second part of the first chapter of the recently published “Digital Pathology 101” book. 

This part of the chapter addresses a question that I keep hearing from those just entering the world of digital pathology: “Will pathologists lose their jobs now, that algorithms can be developed to diagnose disease?”

The short answer is “No”.

Keep reading for the explanation why not.

The Rise of Deep Learning

One of the most notable trends has been the rise of deep learning and AI in digital pathology. These advanced techniques are being embraced by the pathology community to analyze complex issues from sclerotic glomeruli through liver fibrosis to different types of cancer. The user-friendliness of new tools powered by deep learning makes it accessible even for non-experts.

Industry Paradigm Shifts  

Several paradigm shifts are occurring in the digital pathology industry:

• Transition from handcrafted algorithms to deep learning
• Shift to cloud-based Software as a Service (SaaS) solutions 
• Movement towards pathologist decision support systems rather than fully autonomous analysis
• Enhanced user-friendliness of digital pathology software
• 
  

Empowering Pathologists

An important change has been the emphasis on empowering pathologists with decision support systems rather than replacing them with algorithms. The goal is to accelerate the case review process without compromising accuracy or integrity. Pathologists remain responsible for the final diagnosis.

Blending Analog and Digital Worlds

Some innovative companies are pioneering solutions to blend traditional microscopes and digital pathology, such as Augmentics' augmented reality microscope cameras or systems used by Smart in Media. This allows professionals to collaborate in real-time and apply algorithms while still using the cherished microscope. 

Personalized Digital Pathology 

The industry has moved away from a one-size-fits-all approach to personalized solutions tailored to each institution's workflow and challenges. This shift leverages the power of deep learning while enhancing user experience.

The trusted microscope remains an essential part of pathology, but digital solutions open new doors for analysis and efficiency. As this field evolves, quality control and understanding the capabilities and limitations of technology is crucial.

Exciting times are ahead in digital pathology! Be sure to listen to the full podcast episode for an in-depth discussion.

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Get the PDF of "Digital Pathology 101" Book here

Get the paper copy  of "Digital Pathology 101" on AMAZON

Read the original blog post "New Trends and Paradigm Shifts in the Digital Pathology Industry"

Watch the "Digital Pathology 101" Book Launch here

Become a Digital Pathology Trailblazer and See you inside the club: Digital Pathology Club Membership

As much as I love Digital Pathology - things that are not always perfect, and the integrations of systems are not always seamless. We don't need to sugar coat it.

And the sooner we start talking about the things that are not so cool, the sooner we will be able to change them.

In this podcast episode I discuss the things that need to be improved with Puneet Pantane, the Co-Founder and Chief Marketing Officer of Crosscope, where he leverages the power of new technologies such as AI, machine learning, and image processing to improve the research, diagnosis and treatment of cancer.

In this episode we cover:

• What is Crosscope? Where is this company and what are they actually doing?
•  What is digital transformation? 
• Who are Crosscope's customers?
•  What is not working in digital pathology?
•  If we had a magic wand that can solve any digital pathology problem, what would NUMBER 1 PROBLEM to solve be?
  • Spoiler alert: Puneet - interoperability of systems
  • Aleks - reinventing the wheel in image analysis
• What digital pathology problems can be fixed immediately (the low hanging fruits)?
• How to  standardize digital pathology in small pieces?
  
  

If you want to learn more about Crosscope, click here

Become a Digital Pathology Trailblazer and See you inside the club: Digital Pathology Club Membership

This episode is brought to you by Aiforia. Thank you Aiforia :)

Today you will learn how Raish Pai, MD, a busy, practicing pathologist from Mayo Clinic developed a complex supervised deep learning tissue image analysis model to quantify visual diagnostic features of colon cancer and in the process developed a model that can predict clinical outcome.

He used the deep learning-based tissue image analysis platform - Aiforia. 

The quantified features included:

• Stromal immune cell Infiltrates
• Immature stroma
• Tumor-Infiltrating Lymphocytes
• Mucin
• Different growth patterns 
• & many others

THIS EPISODE'S RESOURCES:

• Podcast with Thomas Westerling-Bui, Aiforia: "What is Validation and how to validate and AI image analysis solution"
• Rish Pai's  publication "Quantitative Pathologic Analysis of Digitized Images of Colorectal Carcinoma Improves Prediction of Recurrence-Free Survival"
• Colon Cancer Family Registry website
• Podcast about the BIG PICTURE initiative "BigPicture - the largest whole slide repository for AI model development in pathology. Where do we stand at month 15/ 72?"

THIS EPISODE'S SPECIAL OFFER  "THE BETA COHORT"

Join and be part of the co-creation of the only online course like this in the digital pathology world "PATHOLOGY 101 FOR TISSUE IMAGE ANALYSIS".

Learn more about the AMAZING OFFER that awaits you when you join the BETA COHORT today!

!!! Limited time offer!!! The discount expires on November 27th 2022

Learn more HERE

Become a Digital Pathology Trailblazer and See you inside the club: Digital Pathology Club Membership

In this episode, we talk with Heather Couture about how to make deep learning models for tissue image analysis more robust to domain shift.

Supervised deep learning has made a strong mark in the histopathology image analysis space, however, this is a data-centric approach. We train the image analysis solution on whole slide images and want them to perform on other whole slide images - images we did not train on.

The assumption is that the new images will be similar to the ones we train the image analysis solution on, but how similar do they need to be? And what is domain and domain shift?

Domain: a group of similar whole slide images (WSI). E.g., WSIs coming from the same scanner or coming from the same lab. We train our deep learning model on these WSIs, so we call it our source domain. We later want to use this model and target a different group of images, e.g. images from a different scanner or a different lab - our target domain.

When applying a model trained on a source domain to a target domain we shift the domain and the domain shift can have consequences for the model performance. Because of the differences in the images the model usually performs worse...

How can we prevent it or minimize the damage?

Listen to Heather explain the following 5 ways to handle the domain shift:

1. Standardize the appearance of your images with stain normalization techniques
2. Color augmentation during training to take advantage of variations in staining
3. Domain adversarial training to learn domain-invariant features
4. Adapt the model at test time to handle the new image distribution
5. Finetune the model on the target domain

Click here to read Heather's full article on making histopathology image analysis models more robust to domain shift.

Visit Pixel Scientia Labs here.

And listen to our previous episode titled "Why machine learning expertise is needed for digital pathology projects" here to learn more about the subjects and learn how Heather and her company can help.

Become a Digital Pathology Trailblazer and See you inside the club: Digital Pathology Club Membership