Network Users
Institutions and Labs Using CENTAURON.NET
Scaling Cancer Pathology by Decentralization
We have a 50% lifetime risk of cancer on average. Pathological diagnostics is making the actual diagnosis in cancer and other major diseases by visual and usually manual interpretation of tissue samples. This US$27 billion market is in the process of being massively disrupted now by digitization and artificial intelligence, bringing new opportunities but also threats to pathologists, researchers and AI specialists in the markets of diagnostics, pharma and research. Up to 80% of repetitive tasks in the market are estimated to be replaced by AI. Until 2030 the need for cancer diagnostics market is doubling. Large parts of the world have no or limited access to this diagnostic knowledge. CENTAURON.NET is a hybrid human-machine network of cancer diagnostic specialists supported by collectively created and maintained artificial intelligence algorithms. We think this new form of decentralized organization is best suited for harvesting the opportunities but avoiding the threats. As the leading global knowledge on cancer is created in Universities and research centers while its application occurs in professionalized pharma and diagnostic labs, CENTAURON.NET will comprise public and private nodes. CENTAURON.NET allows to create a global and powerful but decentralized diagnostic AI-based resource for cancer diagnostics. In this extremely fast developing field, only interdisciplinary collaboration can overcome individual limits in medical and technical expert knowledge. Only decentralization can provide robust and valid results. This way, decentralization will set the new medical standards in the coming area of digital medicine.
Digitizing Cancer Diagnostics
Successful cancer prevention and therapy requires excellent diagnostic know how. It is based on an old medical ecosystem developed and run by experts from pathology, cytology, epidemiology and oncology. Digital pathology is the new kid here: Glass slides with the patients’ tissue samples can now be digitized with high-throughput at microscopic scale, meaning images become available which can be analyzed with machine learning. We link digital pathology and machine learning to blockchain.
Machine Learning
Artificial Intelligence (AI) or Machine Learning (ML) with Deep Learning allows training complex and multi-layered Deep Learning Networks (DLNs). We, and others, have shown that such DLNs can outperform human decision making in digital pathology. But DLNs depend on large sets of images, annotated by experts. We aim here at building a leading global image collection in digital pathology for developing DLNs. This way we will provide a cancer diagnostics platform with robust and superior quality.
Blockchain & Tokenization
Token technologies like blockchain or distributed ledgers allow communities of people to build publicly traceable collections of digital assets with each contribution having a specific value. By their intrinsic value creation, crypto-currencies induce ecosystems. Our ecosystem rewards people for the mass digitization of tissue cancer images and the development of machine-learning based algorithms. This will disrupt the old ecosystem and create a machine supported, high-efficiency and high-quality diagnostic system that is globally available.
Portfolio
cAPPS (Cancer Classifying Apps) & Services
Get in Touch
Please contact us if you are curious:
Steinbeis Center for Medical Systems Biology (STCMED), Heidelberg, Germany.
Send an e-mail:
contact@centauron.net