Philips and LabPON Plan to Create World's Largest Pathology Database of Annotated Tissue Images for Deep Learning

PhilipsRoyal Philips (NYSE: PHG, AEX: PHIA) and LabPON, the first clinical laboratory to transition to 100% histopathology digital diagnosis, have announced its plans to create a digital database of massive aggregated sets of annotated pathology images and big data utilizing Philips IntelliSite Pathology Solution (1). The database will provide pathologists with a wealth of clinical information for the development of image analytics algorithms for computational pathology and pathology education, while promoting research and discovery to develop new insights for disease assessment, including cancer.

Deep learning algorithms have the potential to improve the objectivity and efficiency in tumor tissue diagnosis. In recent years, 'deep learning' techniques for image analysis have quickly become the state of the art in computer vision and has surpassed human performance in a number of tasks (2). The challenge for executing deep learning techniques is having access to a database with sufficient high volume and high quality data from which to develop the algorithms. As one of the largest pathology laboratories in the Netherlands, LabPON will contribute its repository of approximately 300,000 whole slide images (WSI) they prospectively create each year to the database. This will contain de-identified datasets of annotated cases that are manually commented by the pathologist, and will comprise of a wide variety of tissue and disease types, as well as other pertinent diagnostic information to facilitate deep learning.

"Deep learning focuses on the development of advanced computer programs that automatically understand and digitally map tissue images in considerable detail: The more data available, the more refined the computer analysis will be," said Peter Hamilton, Group Leader Image Analytics at Philips Digital Pathology Solutions. "Together, LabPON and Philips have the competence and skills to realize this."

During a time where the pathologist shortage is mounting and cancer caseloads are increasing (3,4), the accurate diagnosis and grading of cancer has become increasingly complex, placing significant pressures on pathology services. Technologies such as computational pathology, could help pathologists with tools to work in the most efficient way possible.

"The role of the pathologist remains important by making the definitive diagnosis, which has a high impact on the patient's treatment. Software tools could help to relief part of the pathologists' work such as identifying tumor cells, counting mitotic cells or identifying perineural and vaso-invasive growth, as well carrying out measurements in a more accurate and precise way," said Alexi Baidoshvili, pathologist at LabPON. "This ultimately could help to improve the quality of diagnosis and make it more objective."

Next to the development of computational algorithms for diagnostic use, Philips intends to make available the database to research institutions and other partners through its translational research platform. This could enable selected parties to interrogate and combine massive datasets with the goal to discover new insights that ultimately could be translated into new personalized treatment options for patients.

About Royal Philips
Royal Philips (NYSE: PHG, AEX: PHIA) is a leading health technology company focused on improving people's health and enabling better outcomes across the health continuum from healthy living and prevention, to diagnosis, treatment and home care. Philips leverages advanced technology and deep clinical and consumer insights to deliver integrated solutions. Headquartered in the Netherlands, the company is a leader in diagnostic imaging, image-guided therapy, patient monitoring and health informatics, as well as in consumer health and home care. Philips' health technology portfolio generated 2016 sales of EUR 17.4 billion and employs approximately 71,000 employees with sales and services in more than 100 countries.

1. Philips IntelliSite Pathology Solution is CE-IVD marked for use in primary diagnosis. In the United States, the Philips IntelliSite Pathology Solution pending review of a request for de novo classification.
2. Kaiming He Xiangyu et al. Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. And LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. "Deep learning." Nature 521, no. 7553 (2015): 436-444.
3. The Royal College of Pathologists, https://www.rcpath.org/profession/workforce/workforce-planning.html, Accessed December 2016.
4. International Agency for Research on Cancer and Cancer Research UK. World Cancer Factsheet. Cancer Research UK, London, 2014.

Most Popular Now

Integrating Care Records is Good. Using …

Opinion Article by Dr Paul Deffley, Chief Medical Officer, Alcidion. A single patient record already exists in the NHS. Or at least, that’s a perception shared by many. A survey of...

Should AI Chatbots Replace Your Therapis…

The new study exposes the dangerous flaws in using artificial intelligence (AI) chatbots for mental health support. For the first time, the researchers evaluated these AI systems against clinical standards...

AI could Help Pathologists Match Cancer …

A new study by researchers at the Icahn School of Medicine at Mount Sinai, Memorial Sloan Kettering Cancer Center, and collaborators, suggests that artificial intelligence (AI) could significantly improve how...

AI Detects Early Signs of Osteoporosis f…

Investigators have developed an artificial intelligence-assisted diagnostic system that can estimate bone mineral density in both the lumbar spine and the femur of the upper leg, based on X-ray images...

AI Model Converts Hospital Records into …

UCLA researchers have developed an AI system that turns fragmented electronic health records (EHR) normally in tables into readable narratives, allowing artificial intelligence to make sense of complex patient histories...

AI Sharpens Pathologists' Interpret…

Pathologists' examinations of tissue samples from skin cancer tumours improved when they were assisted by an AI tool. The assessments became more consistent and patients' prognoses were described more accurately...

AI Tool Detects Surgical Site Infections…

A team of Mayo Clinic researchers has developed an artificial intelligence (AI) system that can detect surgical site infections (SSIs) with high accuracy from patient-submitted postoperative wound photos, potentially transforming...

Forging a Novel Therapeutic Path for Pat…

Rett syndrome is a devastating rare genetic childhood disorder primarily affecting girls. Merely 1 out of 10,000 girls are born with it and much fewer boys. It is caused by...

Mayo Clinic's AI Tool Identifies 9 …

Mayo Clinic researchers have developed a new artificial intelligence (AI) tool that helps clinicians identify brain activity patterns linked to nine types of dementia, including Alzheimer's disease, using a single...

AI Detects Fatty Liver Disease with Ches…

Fatty liver disease, caused by the accumulation of fat in the liver, is estimated to affect one in four people worldwide. If left untreated, it can lead to serious complications...

AI Matches Doctors in Mapping Lung Tumor…

In radiation therapy, precision can save lives. Oncologists must carefully map the size and location of a tumor before delivering high-dose radiation to destroy cancer cells while sparing healthy tissue...

Meet Your Digital Twin

Before an important meeting or when a big decision needs to be made, we often mentally run through various scenarios before settling on the best course of action. But when...