Cloud computing with machine learning for cancer diagnosis

It is observed recently that larger percent of forex traders are now interested in automatic forex system trading which has led to the development of so many expert advisors or robot to take control of their currency trading.

Cloud computing with machine learning for cancer diagnosis

Aindra IS autostainer has helped me achieve consistent and high quality staining. Along with being hassle free, It is also compact and occupies very little space.

It has significantly more reduced our turn around time for reporting. It's easy to use and can be carried around with ease -- Ms.

Kameshwari, Cancer Care India. Aindra IS has helped us achieve unparalleled staining quality. It is an easy to use, walk-away system -- Dr.

Machine learning - Wikipedia

Malathi, Kidwai Memorial Institute of Oncology. That is the number of people on the planet who today, do not have access to a reliable and high quality healthcare. While inaccessibility is the problem facing the developing world, expensive machines and treatment methods are driving healthcare costs uncomfortably high in the developed countries.

And we want to change this broken healthcare system.


We at Aindra, are a passionate bunch of technophiles. At, Aindra Systems, our goal is to democratise access to quality healthcare, by using deep technology to realise our vision of accessible and affordable healthcare solutions.

As technology enthusiasts, we believe the impact of leveraging technologies like Artificial Intelligence in the right way, can deliver more smarter, quicker and reliable systems that will help overcome the existing challenges of the healthcare system.

Our AI platform Astra, is built to detect critical illnesses such as Cancer. Astra is a powerful platform that can be extended to build detection tools for a number of critical illnesses. As the first beachhead, we have developed a point-of-care detection system for Cervical Cancer that is quick, affordable and accessible.

Cloud computing with machine learning for cancer diagnosis

CervAstra will enable women to walk-in to their nearest primary health center, get screened and walk out with their reports. It is that simple. Our intelligent screening system CervAstra, tailwinds the development and striking benefits of computational pathology.

The biggest reason is because today, multiple factors have converged to create an environment where AI can make discoveries and judgments leading to faster and more accurate diagnoses than humans can perform. It significantly reduces the turnaround time for reports and also enables pathologists to look at only the relevant data.

Farther outreach Healthcare Inclusion. Diagnostic centers are concentrated in the larger cities, whereas most of the population resides in smaller cities or villages.Artificial intelligence in the database can help doctors make diagnosis and help streamline manufacturing.

and by bringing cloud computing capabilities to the edge through virtual machines and Docker-style containerization. You may want to use machine learning in the cloud to run predictive analytics or computational understanding, but.

Skin cancer can be detected more quickly and accurately by using cognitive computing-based visual analytics, researchers at IBM Research have found, in collaboration with New York's Memorial Sloan. IBM Watson for Genomics. Bringing the promise of precision medicine to more cancer patients, Watson can interpret genetic testing results faster and with greater accuracy than manual efforts.

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Apr 03,  · In this blog post, we showed how to use Azure Machine Learning to train and test an AI model and create an intelligent iOS app.

Such apps can help with time-critical decisions at the edge, referring to the cloud only if more intensive computation or historical analysis is needed. InnerEye is a research project that uses state of the art machine learning technology to build innovative tools for the automatic, quantitative analysis of three-dimensional radiological images.

This is a must read for those interested in cancer. The author uses a historical framework to explain the science, and makes a strong case for cancer as a metabolic disease (as contrasted to a genetic one).

Project InnerEye - Medical Imaging AI to Empower Clinicians - Microsoft Research