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.
Digital Pathology
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.
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.
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 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.
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.
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:
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
The Ohio State University, USA
Dan Ciresan
CEO, Conndera Research,
Romania
Romania
Jeroen van der Laak
Associate Professor at Radboud UMC,
Nijmegen, the Netherlands
Nijmegen, the Netherlands
Junya Fukuoka
MD.PhD. Chair, Professor,
Dept. of Pathology,
Nagasaki University, Japan
Dept. of Pathology,
Nagasaki University, Japan
Fredrik Pontén
MD, PhD, Dept. of Immunology,
Genetics & Pathology,
Uppsala University, Sweden
Genetics & Pathology,
Uppsala University, Sweden
Henning Müller
Prof. Dr.
University of Applied Sciences and
Arts Western Switzerland
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.
Top placements in challenges
Our technological advances have landed us top positions in prestigious competitions using deep learning in digital pathology.
In Tumor Proliferation Assessment Challenge (TUPAC16), we were awarded 2nd place for our breast cancer mitosis detection algorithms and their ability to correctly predict tumor proliferation in whole-slide images. Our results outshone those of many highly reputable academic teams as well as research groups from companies such as IBM and Microsoft.
In Tumor Proliferation Assessment Challenge (TUPAC16), we were awarded 2nd place for our breast cancer mitosis detection algorithms and their ability to correctly predict tumor proliferation in whole-slide images. Our results outshone those of many highly reputable academic teams as well as research groups from companies such as IBM and Microsoft.
In CAMELYON17, the goal was to evaluate new and existing algorithms for automated detection and classification of breast cancer metastases in whole-slide images – a task with high clinical relevance, and which normally requires extensive microscopic assessment by pathologists. ContextVision’s algorithms, now further refined and used in INIFY, held a very impressive 2nd place during 2018.
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)
(The paper will be published in Springer LNCS)