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Discover

the

technology

that

sets

us

apart

Dataset

Our technology is powered by exclusive access to diverse datasets, including hematoxylin & eosin-stained slides, immunohistochemistry-stained slides, clinical data, as well as gene expression data. Using these routinely available histology images and associated nonimaging data, we develop robust and accurate state-of-the-art AI models. This comprehensive training ensures our models deliver reliable diagnostic, predictive and prognostic insights, ultimately improving patient outcomes.

Biomarker Detection

With each cancer presenting unique characteristics, precise biomarker detection becomes essential for creating personalized treatment plans. At StratifAI, we excel in biomarker detection using advanced transformer-based AI models trained on routinely available histology images. By leveraging slide-level labels, we bypass the burdensome and time-consuming manual annotation process, enabling rapid development of customized AI models. Our approach has been validated through numerous clinical studies and was recently published in Nature Protocols¹.

Foundation Models

Deep learning-based AI models usually require extensive amounts of annotated training data to achieve high precision. To overcome this challenge, we developed a proprietary foundation model using self-supervised learning, enabling us to leverage hundreds of thousands of unannotated data samples to extract meaningful feature representations of our data. This technology significantly reduces the need for labeled data in training our biomarker detection models, allowing us to develop highly precise biomarker tools more efficiently.

Multimodal AI

At StratifAI, we leverage the power of multimodal AI to revolutionize cancer diagnostics and treatment. Our advanced models are capable of integrating data from various sources, such as histology images and clinical records, to provide a comprehensive understanding of the tumor microenvironment and each patient’s cancer profile. This holistic approach enables us to unlock the true potential of AI, elevating diagnostic, predictive, and prognostic accuracy to new heights.

Dataset

Our technology is powered by exclusive access to diverse datasets, including hematoxylin & eosin-stained slides, immunohistochemistry-stained slides, clinical data, as well as gene expression data. Using these routinely available histology images and associated nonimaging data, we develop robust and accurate state-of-the-art AI models. This comprehensive training ensures our models deliver reliable diagnostic, predictive and prognostic insights, ultimately improving patient outcomes.

Biomarker Detection

With each cancer presenting unique characteristics, precise biomarker detection becomes essential for creating personalized treatment plans. At StratifAI, we excel in biomarker detection using advanced transformer-based AI models trained on routinely available histology images. By leveraging slide-level labels, we bypass the burdensome and time-consuming manual annotation process, enabling rapid development of customized AI models. Our approach has been validated through numerous clinical studies and was recently published in Nature Protocols¹.

Foundation Models

Deep learning-based AI models usually require extensive amounts of annotated training data to achieve high precision. To overcome this challenge, we developed a proprietary foundation model using self-supervised learning, enabling us to leverage hundreds of thousands of unannotated data samples to extract meaningful feature representations of our data. This technology significantly reduces the need for labeled data in training our biomarker detection models, allowing us to develop highly precise biomarker tools more efficiently.

Multimodal AI

At StratifAI, we leverage the power of multimodal AI to revolutionize cancer diagnostics and treatment. Our advanced models are capable of integrating data from various sources, such as histology images and clinical records, to provide a comprehensive understanding of the tumor microenvironment and each patient’s cancer profile. This holistic approach enables us to unlock the true potential of AI, elevating diagnostic, predictive, and prognostic accuracy to new heights.

Data

Our technology is powered by exclusive access to diverse datasets, including hematoxylin & eosin, immunohistochemistry and FISH images, alongside clinicopathologic data and gene expression data. Using our high quality microscopy images and associated data, we develop robust and accurate state-of-the-art AI models. This comprehensive training ensures our models deliver reliable diagnostic, predictive and prognostic insights, ultimately improving patient outcomes.

Spatial
biomarker discovery

With each cancer presenting unique characteristics, precise biomarker detection becomes essential for creating personalized treatment plans. At StratifAI, we excel in biomarker detection using advanced transformer-based AI models trained on routinely available histology images. By leveraging slide-level labels, we bypass the burdensome and time-consuming manual annotation process, enabling rapid development of customized AI models. Our approach has been validated through numerous clinical studies and was recently accepted in Nature Protocols¹.

Multimodal
digital oncology

At StratifAI, we leverage the power of multimodal AI to revolutionize cancer prognostication and treatment. Our advanced models are capable of integrating data from various sources, including diverse medical imaging data and clinical records, to provide a comprehensive understanding of the tumor microenvironment and an individualized cancer characterization. Our holistic approach enables us to unlock the true potential of AI, elevating diagnostic, predictive, and prognostic accuracy to new heights.

Key Facts

Bio­marker detection models in our AI-suite.

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Digitized slides available
for model training

Unique cancer types from which we can extract biomarkers.

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Digitized slides available
for model training

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Bio­marker detection
models in our AI-suite.

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Unique cancer types from which
we can extract biomarkers.

Read our publications

References
  1. Please find our preprint under: El Nahhas, O. S. M., van Treeck, M., Wölflein, G., Unger, M., Ligero, M., Lenz, T., Wagner, S. J., Hewitt, K. J., Khader, F., Foersch, S., Truhn, D., & Kather, J. N. (2023). From Whole-slide Image to Biomarker Prediction: A Protocol for End-to-End Deep Learning in Computational Pathology. arXiv. https://doi.org/10.48550/arXiv.2312.10944