Digital Pathology

Already a world leader in image enhancement, ContextVision is now taking its medical analysis know-how into a new field. With the development of our INIFY™ portfolio, we aim to empower pathologists in the new digital era.

Power to the pathologists

Today’s pathologists face an ever-increasing workload. By providing support tools that fully explore the advantages of new digital technology – drawing on the very latest breakthroughs in AI and deep learning – we aim to empower you in your everyday workflow.

INIFY™ Prostate – powerful, AI-based support software for today’s digital pathology

After years of research and development, working closely together with world-renowned pathologists and a brilliant team of deep learning experts, we are proud to unveil the first product in our INIFY digital pathology portfolio.

INIFY Prostate is a powerful AI-based software that outlines and quantifies suspected cancerous areas in a series of prostate biopsies. It pre-sorts slides in a worst-first order, allowing you to get right to work on the most relevant areas.

The user-friendly viewer, with its prostate-specific design and a wide range of smart tools and handy features, makes it quick and easy for uropathologists to check, confirm and measure suspected cancer and non-cancer structures.

MasterAnnotation™ – for accurate, reliable performance

The INIFY algorithms are trained on data from our MasterAnnotation method – a unique, patent-filed innovation developed by ContextVision. 
MasterAnnotation is our standardized procedure for generating training data. Briefly, a high-resolution multiplex immunofluorescence overlay is used to generate a precise annotation – an augmented ground truth – for the development and optimization of the INIFY algorithms.

Power up your workflow

INIFY Prostate – designed for clinical pathology

With INIFY Prostate, we wanted to create a tool that truly supports uropathologists’ everyday workflow when handling prostate biopsies. That’s why we developed the INIFY Prostate viewer together with uropathologists. Every detail has been carefully planned and tested to provide a smooth, intuitive user experience that fits your individual needs.
”The measurement features will improve the accuracy in my reports.”
Dr. Michal Ostrowski & Dr. Kinga Sikorski-Mali, ALAB, Warsaw
”This tool is so easy to use.”
Dr. Margot Dupeux, Hospital Bicetre, Paris
”Incredibly fast panning and zooming.”
Dr. Witold Rezner, ALAB, Warsaw

Seamless integration

Knowing that every clinical environment and pathologist has their own requirements and preferences, we provide a customizable integration as well as an optional, built-in case manager.

The latest breakthroughs in deep learning

The complex patterns in histopathology tissue samples make them challenging to evaluate with classic AI algorithms. However, new machine learning technology has proved to be very powerful for pattern recognition – handling tasks that are highly challenging even for the human brain.

INIFY Prostate is built on deep learning algorithms, which in turn draw on the latest advances in Convolutional Neural Networks (CNNs).
INIFY’s sophisticated algorithms have been developed in collaboration with deep learning experts and pathology specialists. Trained with our patent-filed MasterAnnotation method for auto-annotation of cancer tissue, they provide accurate, reliable and objective outcomes, helping pathologists conclude the diagnosis for each individual patient.

Supporting the pathologist

Today’s pathologists face an ever-increasing number of pathology samples, as well as repetitive, time-consuming and often tedious analysis process. Combined with an all-too-common shortage of qualified pathologists, this risks creating bottlenecks in the treatment process, not to mention stressful working environments.

INIFY powers up the diagnostic workflow, allowing pathologists to realize their full potential as skilled specialists and make the most of their valuable time.

The experience behind our products

Our Digital Pathology team includes many highly experienced specialists from a broad range of fields: experts in immunology and biology, highly skilled project managers, QA/regulatory experts, software engineers, experts on deep learning and AI as well as marketers with a solid business focus. In addition, we have impressive external networks of pathologists, research labs, industry collaborators and other partners. 

The development of our Digital Pathology portfolio builds on the findings of SL-DESUTO, a now finished Eurostars-sponsored research collaboration between ContextVision and the University of Applied Sciences Western Switzerland. ContextVision Advisory Board plays a key role in further strategic research & development within the business unit, providing us with unique and invaluable insights and feedback. The board features some of the most prominent names in the field:

Anil Parwani
Professor of Pathology at
The Ohio State University, USA
Dan Ciresan
CEO, Conndera Research,
Romania
Jeroen van der Laak
Associate Professor at Radboud UMC,
Nijmegen, the Netherlands
Junya Fukuoka
MD.PhD. Chair, Professor,
Dept. of Pathology,
Nagasaki University, Japan
Fredrik Pontén
MD, PhD, Dept. of Immunology,
Genetics & Pathology,
Uppsala University, Sweden
Henning Müller
Prof. Dr. 
University of Applied Sciences and
Arts Western Switzerland

I am impressed by the company’s vision and their amazing efforts within AI and machine learning for radiology. I believe that these strengths can help solve some of the current clinical issues that we encounter in pathology today.
Professor Anil Parwani
Ohio State University

Top placements in challenges

ContextVision
– a trusted name in diagnostics software

ContextVision is a medical technology software company that specializes in image analysis and artificial intelligence. As the global market leader within image enhancement, ContextVision is a software partner to leading medical imaging manufacturers
around the world. Its cutting-edge technology helps doctors accurately interpret medical images, a crucial foundation for better diagnosis and treatment. As an industry pioneer for more than 30 years, ContextVision has developed state-of-the-art capabilities in the latest artificial
intelligence technologies. By combining these with its well-established GOP technology, the company is introducing a new generation of image enhancement products. ContextVision is also approaching the growing digital pathology market with new AI-based decision support tools for pathologists.

ContextVision holds an ISO 13485:2016 certification.

The company is based in Sweden, with local representation in the U.S., Russia, Japan, China and Korea. ContextVision is listed on the Oslo Stock Exchange under the ticker COV.

News and upcoming events

Publications

Nikolay Burlutskiy, Feng Gu, Lena Kajland Wilen, Max Backman, Patrick Micke.

A Deep Learning Framework for Automatic Diagnosis in Lung Cancer

International Conference on Medical Imaging with Deep Learning (MIDL2018 in Amsterdam).
Lars Björk, Jonas Gustafsson, Feria Hikmet Noraddin, Kristian Eurén, Cecilia Lindskog.

A new high-throughput auto-annotation method to detect and outline cancer areas in prostate biopsies

Presentation at ECDP 2018 Helsinki, Finland.
Peter Bandi, Oscar Geesink, Ludwig Jacobsson, Martin Hedlund et al.

From detection of individual metastases to classification of lymph node status at the patient level: the CAMELYON17 challenge

IEEE Transaction on Medical Imaging PP(99):1-1, To be released.
Feng Gu, Nikolay Burlutskiy, Mats Andersson, and Lena Kajland Wilén.

Multi-Resolution Networks for Semantic Segmentation in Whole Slide Images

MICCAI 2018 – COMPAY Workshop on computational pathology, Granada, Spain.
Mikael Rousson, Martin Hedlund, Mats Andersson, Ludwig Jacobsson, Gunnar Lathen, Bjorn Norell, Oscar Jimenez-del-Toro, Henning Mueller, Manfredo Atzori.

Tumor proliferation grading from whole slide images

SPIE Medical Imaging, 2018, Houston, Texas, United States.
Sebastian Otalora, Manfredo Atzori, Vincent Andrearczyk, and Henning Muller.

Image Magnification Regression Using DenseNet for Exploiting Histopathology Open Access Content

MICCAI 2018 – COMPAY Workshop on computational pathology, Granada, Spain.
Sebastian Otálora, et. al.

Determining the scale of image patches using a Deep Learning Approach

ISBI 2018. Washington D.C., USA, 2018.
Otálora s., Andrearczyk V., Atzori M., Müller H.

BIWGAN: Learning stable adversarial representations for prostate histopathology images

Medical Imaging Summer School 2018: Medical Imaging meets Deep Learning. Favignana, Italy.
Oscar Jimenez del Toro, Manfredo Atzori, Sebastian Otálora, Mats Andersson, Kristian Eurén, Martin Hedlund, Peter Rönnquist and Henning Müller.

Convolutional neural networks for an automatic classification of prostate tissue slides with high-grade Gleason score

SPIE Medical Imaging, pages 101400O-101400O-9, 2017.
Oscar Jimenez del Toro, Sebastian Otálora, Mats Andersson, Kristian Eurén, Martin Hedlund, Mikael Rousson, Henning Müller and Manfredo Atzori.

Elsevier book on Texture Analysis, chapter Analysis of Histopathology Images

From Traditional Machine Learning to Deep Learning, 2017.
Oscar Jimenez del Toro, Sebastian Otálora, Manfredo Atzori and Henning Müller.

Deep Multimodal Case-Based Retrieval for Large Histopathology Dataset

MICCAI 2017 workshop on Patch-based image analysis, Quebec City, Canada, 2017.
(The paper will be published in Springer LNCS)

Digital Pathology

Lena Kajland Wilén

Director Business Unit Digital Pathology


Kristian Eurén

Product Manager Digital Pathology


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