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The Pathologist / Issues / 2026 / October / PathVisions 2026: Scaling Digital Pathology
Digital and computational pathology Technology and innovation Digital Pathology

PathVisions 2026: Scaling Digital Pathology

Improving digital scaled solutions in pathology with free and open-sourced resources

10/19/2026 Video 2 min read
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Scaling up computational pathology doesn't have to cost the earth. Here Mohannad Hussain previews a few of the free-to-use and open-source resources he's introducing at Pathology Visions 2026, and advocates the power of connectathons in refining tools for clinical use.

Mohannad Hussain is Strategic Advisor at Radical Imaging.

The following transcript has been edited for clarity.

Hello everyone. My name is Mohannad Hussain, and I'm here to speak to you about my presentation at the Pathology Visions 2026 conference. I'm a Strategic Advisor for Radical Imaging and my talk is going to be centered on a topic that's near and dear to my heart; which is related to open-source solutions and existing free solutions in digital pathology.

My talk is going to focus on a number of free and open-source resources. I'll start by giving a quick background that just establishes a common understanding of what open source means, what it actually does in real life, and then I'm going to explore a number of options. One of them is the DICOM microscopy viewer, which is a framework for viewing microscopy DICOM images. We'll also talk about more finished products like the slim viewer, the OHIF viewer, and last but not least, a resource known as the NIH imaging data commons which is a big repository of multimodality images from pathology, radiology, etc.

I'm also going to be talking about how these are constantly refined. A resource like imaging data commons is getting constant refinement because it's getting a lot of usage throughout the year. But we also take these toolkits and solutions to connectathons like the recent DICOM work group 26 connectathon.

I'll also cover common themes for recent improvements: AI annotation markup tooling, AI output visualization, multi-channel multiplex imaging, diagnostic image fidelity, performance, and of course last but not least is reliability and robustness. And we wouldn't be keeping up with the times if we didn't cover something related to Large Language Models (LLMs). One of the things I'm also going to be covering is how LLMs can be used in conjunction with some of these resources.

I hope you get to enjoy my talk and you find it informative. Thank you.

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