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On Sep 2, 2019, Matrix AI Network has officially launched the Matrix Developer Portal. The platform provides developers with the tools and data needed to build apps. The official website is now live at https://dev.matrix.io/.

The Matrix Developer Portal

In the first phase, the developer portal will provide an API with a feed of core transactional information enabling developers to build apps about the operations of the mainnet. In the second phase, additional tools will be provided to build apps that integrate smart contracts.

The Matrix developer portal already provides developers with an information feed to create applications that interface with the fundamental info of the Matrix main chain. The center will soon support the development of applications that integrate the smart contracts.

Phase one

The Matrix developer portal offers an API for a direct information feed from the database of the Matrix blockchain mainnet.

Network: To build primary nodes embedded in the Matrix mainnet or testnet, as well as how to establish a private network. Developers can get full instructions on how to embed in the Matrix mainnet, testnet, or establish a private network.

Tools: Developers can utilize Javascript SKD or Java SDKs to quickly build applications.

API: There are three methods for pulling the blockchain data, using RPC, Javascript or Java APIs.

Data: This module explains how developers can connect with the Matrix open source database and table editor.

Phase two

The Matrix developer portal will continue to roll out smart contracts, examples of Dapps, Dapp platform transfer solutions, and FAQs. This forthcoming module will help developers build Dapps with smart contract integration.

Exchange and Interaction

If you encounter any issues while using APIs from the developer portal, please contact the official Reddit account r/THEMATRIXAI. Matrix has issued a post named “Common Problems of Matrix Developer Portal and How to solve them” to compile solutions and discussions.

Responding to Questions and Difficulties

If you encounter issues while using the developer portal, you can email questions to public@matrix.io or contact a member of the Matrix team on Telegram.

Medical imaging, the process of creating a visual representation of the interior of a body for clinical analysis and medical intervention, plays an important part in efficient disease diagnosis. Among various types of medical imaging, tomography, or imaging by sections, is the best known. Its main methods are X-ray computed tomography (CT), positron emission tomography (PET) and magnetic resonance imaging (MRI). Although traditional MRIs and CTs are commonly used in body scans, they are being improved to unleash greater potential and more accurate and efficient AI-medical diagnosis. This article will introduce the way AI technology is shaping and improving medical imaging and the contributions made by CT scans and MRI.

CT scans

Matrix AI Network has been leveraging AI technology to advance its medical projects, like small cell lung cancer (SCLC) diagnosis and rib fracture diagnosis based on CT scans. For the projects above, Matrix AI Network has addressed two major problems respectively: one is to better identify SCLC cells in high-resolution CT scans (20000 x 20000) by analyzing macro- and micro-data, while the other is to greatly improve the accuracy and efficiency of diagnosis and treatment by resourcing the original CT scan and generating a 3D image.

MRI

Microsoft’s Quantum team cooperates with Case Western Reserve University to enhance their approach to detecting cancerous tumors and then improving the accuracy of MRI results in less time by introducing an approach named magnetic resonance fingerprinting. This uses a constantly varying sequence of pulses to get a single, unified exam.

Facebook and NYU School of Medicine have launched a collaborative research project named fast MRI, making MRI scans up to 10 times faster. If this effort is successful, it will make MRI technology available to more people, expanding access to this key diagnostic tool.

A Canadian research team successfully communicated with vegetative state patients via MRI.

Scans of hidden consciousness of a vegetative state patient

Delineation of subcortical neuroanatomy

With such contributions to the current AI-medical diagnosis, medical imaging is beyond doubt valuable. As it employs the latest form of high-quality data, how to explore its potential in the fields of blockchain and AI technology becomes the next question. Based on the current rib fracture and SCLC projects, Matrix AI Network has been applying the visual data into AI model training, forming a virtuous circle where medical diagnosis systems and AI-medical platforms can be constantly improved.

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