If you’re developing AI‑driven health technologies or medical robotics, you are moving fast – but clinical adoption and regulatory approval depend on something far less glamorous: the quality of your data.
Tag - AI
Nadine Lang-Richter
Tobias Sebastian Zech
Train your own AI with the Segmentation AI Author: Ovarian Cancer Example
This MIKAIA® University app note demonstrates how to quickly and interactively train your own AI using just a few training annotations, all in a matter of minutes. In the app center, select the Segmentation AI Author...
Michaela Benz
Segmentation AI Author Example: Train H&E Vulvar Cancer AI Based on Aligned IHC Serial Sections
This MIKAIA® University app note illustrates how to interactively train a new H&E segmentation AI with serial sections, without having to annotate tumor regions. Instead, an IHC epithelial marker (pan-cytokeratin...
IHC Cell Detection App explained
The IHC Cell Detection App features a universal subcellular IHC cell AI that is compatible with a wide range of markers, tissues, and cell types. Our 23-minute video tutorial guides MIKAIA® step by step through the app...
Volker Bruns
MIKAIA IHC Cell Quantification AI Gallery
This gallery illustrates for a range of different tissue types and IHC markers the cell analysis quality of MIKAIA‘s IHC Cell Detection App. The app first unmixes the stain components, then uses an AI to delineate...
Digital Pathology AI Companies — List of Commercial AIs (CE-IVDR, FDA, RUO)
NEW: This article and the downloadable list was updated on July 15th 2025 2022 – 2024 have been breakthrough years for AI in digital pathology. Many new startups, backed by lots of venture capital, as well as...
Overview of MIKAIA’s Spatial Analysis Apps for Multiplexed Immunofluorescence Slides
A strong suit of MIKAIA® is its wide portfolio of analysis apps for multiplexed immunofluorescence (mIF) / spatial proteomics. It can be used for both low-plex and high-plex panels and is compatbile with image formats...
More than AI-based Analysis: Discover Spatial Patterns in your Whole Slide Images with MIKAIA
How can the advanced AI analysis capabilities of MIKAIA® assist researchers and clinicians in analyzing their digital pathology and spatial biology data? This talk provides an overview of MIKAIA’s advanced...
Cognitive Load Assessment: Uncertainty as a Pathway to Robustness
In an increasingly complex world, users must process more and more information which makes information processing and decision-making all the more complex. Understanding cognitive load (i.e., the measurement of the...
Analyzing MACSima 47-plex mIF with MIKAIA: AI Cell Segmentation + Cell Typing + Cell-cell Connections + Cellular Neighborhoods
This MIKAIA® app note demonstrates how to analyze a 47-plex spatial proteomics scan created with MACSima ™ (by Miltenyi Biotec), utilizing sequential immunofluorescence for the analysis: Instrument: MACSima™ by Miltenyi...
