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73: Digital Pathology 101 Chapter 3 | Image Analysis, Artificial Intelligence, and Machined Learning in Pathology
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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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