Easy-to-use AI-based deep learning is now integrated as the standard analysis methodology in our best-in-class analysis software. Designed for anyone, it allows tissue-based researchers to tackle both simple and complex datasets across a wide variety of applications. In the past, only image analysis experts could analyse complex tissues.
Our new analysis packages include powerful pre-trained nuclei segmentation algorithms suitable for brightfield and fluorescence applications which can be further tuned for even more specificity, and deep-learning-based tissue segmentation to find tissues of interest, exclude artifacts, and enable scoring/counting within specific tissue compartments. Now, anyone with the understanding of tissue morphology can train an AI-based deep learning network to get accurate and reproducible data, making it easy to generate reliable quantitative results needed for breakthrough discoveries and publications.
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- How the use of deep learning improves the analysis of digital pathology images
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- The use of deep learning to segment tissues, find artifacts, and localize scoring/counting to specific regions of tissue
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- Using pre-trained deep learning networks to segment nuclei across a range of tissue and staining types
David Mason, Senior Technical Specialist, Visiopharm
Dave Mason is a senior technical specialist in image analysis, supporting Visiopharm’s UK and European sales team. He has a background in cell biology and microbiology and has spent over a decade in academia, specializing in light microscopy and digital image analysis.
Fabian Schneider, PhD, Product Manager Research, Visiopharm
Dr. Fabian Schneider is part of Visiopharm’s R&D and Product Management team, responsible for phenotyping products as well as service projects for custom APP development. Fabian has over 10 years of international experience in cancer biology and immuno-oncology, working in academic research labs, clinical research teams and computational pathology groups in both academia and biopharma. Fabian received his Dr phil. nat. in Cell Biology in 2011 from the Johan Wolfgang Goethe University Frankfurt, Germany.