AI News, Data Tagging in Medical Imaging – Diving Deep into the Processes

Data Tagging in Medical Imaging – Diving Deep into the Processes

In the previous post of the series, Data Tagging in Medical Imaging, we gave you an overview of the kind of processes that you must put in practice to scale your data tagging engine for leveraging AI in healthcare. In this blog, we will thoroughly discuss how to come up with these processes and things to consider before finalizing and formulating these processes.

Ideally, this is all one large workflow process designed to help you annotate your medical dataset in a faster, scalable and accurate way.

Let’s discuss these aforementioned processes in details below:  It is really critical to define how the data will flow through all the stakeholders at different stages.

The entire purpose of annotating all the medical data is to provide it to the Data Science team for them to build the model.

In this blog, we discussed the initial requirements of setting up a scalable tagging engine and core processes involved.

8 Useful Databases to Dig for Data (and 100 more)

You already know that data is the bread and butter of reports and presentations.

To make your life easier, we’ve put together a list of useful databases that you can use to find the data you seek.

This database contains large datasets, consisting virtually all the public data collected by the United Nation.

Some other topics included are: Data.gov is leading the way in democratizing public sector data and driving innovation.

Citing journal publishers, universities research papers, and other scholarly materials do not just make your content looks smarter, but as well as more trustworthy.

It’s a good place to explore data related to economics, healthcare, food and agriculture, and the automotive industry.

Since our original post, we’ve come across a few more sources of data that might be useful for you: You can also get a crazy amount of datasets and related information from Datamob.

Now that you have an abundance of data on hand, find out how to avoid these common mistakes when transforming them into infographics.

Path to 1 Billion Time Series: InfluxDB High Cardinality Indexing Ready for Testing

One of the long-standing requests we’ve had for InfluxDB is to support a large number of time series.

While we currently have customers with tens of millions of time series, we’re looking to expand to hundreds of millions and eventually billions.

This work has been in the making since last August and represents the most significant technical advancement in the database since we released the Time Series Merge Tree storage engine last year. Read on for details on how to enable the new index engine and what kinds of problems this will open up InfluxDB to solve.

The TSM engine that we built in 2015 and 2016 was an effort to solve the first part of this problem: getting maximum throughput, compression, and query speed for the raw time series data.

This meant that for every measurement, tag key/value pair, and field name there was a lookup table in memory to map those bits of metadata to an underlying time series.

Much like the TSM engine for raw time series data we have a write-ahead log with an in-memory structure that gets merged at query time with the memory-mapped index.

Background routines run constantly to compact the index into larger and larger files to avoid having to do too many index merges at query time. Under the covers, we’re using techniques like Robin Hood Hashing to do fast index lookups and HyperLogLog++ to keep sketches of cardinality estimates.

There is more work we have to do to tune the compaction process so that it requires less memory along with much testing and bug fixing (watch the tsi label to keep track).

Users will be able to track ephemeral time series like per process or per container metrics, or data across a very large array of sensors.

Tag (metadata)

the structure of both top-down and bottom-up taxonomies may be either hierarchical, non-hierarchical, or a combination of both.[3]:142–143 Some researchers and applications have experimented with combining hierarchical and non-hierarchical tagging to aid in information retrieval.[7][8][9] Others are combining top-down and bottom-up tagging,[10] including in some large library catalogs (OPACs) such as WorldCat.[11][12]:74[13][14] When tags or other taxonomies have further properties (or semantics) such as relationships and attributes, they constitute an ontology.[3]:56–62 Metadata tags as described in this article should not be confused with the use of the word 'tag' in some software to refer to an automatically generated cross-reference;

In 2003, the social bookmarking website Delicious provided a way for its users to add 'tags' to their bookmarks (as a way to help find them later);[2]:162 Delicious also provided browseable aggregated views of the bookmarks of all users featuring a particular tag.[19] Within a couple of years, the photo sharing website Flickr allowed its users to add their own text tags to each of their pictures, constructing flexible and easy metadata that made the pictures highly searchable.[20] The success of Flickr and the influence of Delicious popularized the concept,[21] and other social software websites—such as YouTube, Technorati, and Last.fm—also implemented tagging.[22] In 2005, the Atom web syndication standard provided a 'category' element for inserting subject categories into web feeds, and in 2007 Tim Bray proposed a 'tag' URN.[23] Many blog systems (and other web content management systems) allow authors to add free-form tags to a post, along with (or instead of) placing the post into a predetermined category.[5] For example, a post may display that it has been tagged with baseball and tickets.

In Apple's macOS, the operating system has allowed users to assign multiple arbitrary tags as extended file attributes to any file or folder ever since OS X 10.9 was released in 2013,[26] and before that time the open-source OpenMeta standard provided similar tagging functionality in macOS.[27] Several semantic file systems that implement tags are available for the Linux kernel, including Tagsistant.[28] Microsoft Windows allows users to set tags only on Microsoft Office documents and some kinds of picture files.[29] Cross-platform file tagging standards include Extensible Metadata Platform (XMP), an ISO standard for embedding metadata into popular image, video and document file formats, such as JPEG and PDF, without breaking their readability by applications that do not support XMP.[30] XMP largely supersedes the earlier IPTC Information Interchange Model.

they are a type of metadata that captures knowledge in the form of descriptions, categorizations, classifications, semantics, comments, notes, annotations, hyperdata, hyperlinks, or references that are collected in tag profiles (a kind of ontology).[41] These tag profiles reference an information resource that resides in a distributed, and often heterogeneous, storage repository.[41] Knowledge tags are part of a knowledge management discipline that leverages Enterprise 2.0 methodologies for users to capture insights, expertise, attributes, dependencies, or relationships associated with a data resource.[3]:251[42] Different kinds of knowledge can be captured in knowledge tags, including factual knowledge (that found in books and data), conceptual knowledge (found in perspectives and concepts), expectational knowledge (needed to make judgments and hypothesis), and methodological knowledge (derived from reasoning and strategies).[42] These forms of knowledge often exist outside the data itself and are derived from personal experience, insight, or expertise.

Knowledge tags are valuable for preserving organizational intelligence that is often lost due to turnover, for sharing knowledge stored in the minds of individuals that is typically isolated and unharnessed by the organization, and for connecting knowledge that is often lost or disconnected from an information resource.[43] In a typical tagging system, there is no explicit information about the meaning or semantics of each tag, and a user can apply new tags to an item as easily as applying older tags.[2] Hierarchical classification systems can be slow to change, and are rooted in the culture and era that created them;

When users can freely choose tags (creating a folksonomy, as opposed to selecting terms from a controlled vocabulary), the resulting metadata can include homonyms (the same tags used with different meanings) and synonyms (multiple tags for the same concept), which may lead to inappropriate connections between items and inefficient searches for information about a subject.[44] For example, the tag 'orange' may refer to the fruit or the color, and items related to a version of the Linux kernel may be tagged 'Linux', 'kernel', 'Penguin', 'software', or a variety of other terms.

Connect the Right People to the Right Data

Waterline provides data catalog and governance applications based on a metadata discovery platform that makes it easy for organizations to discover, organize, and surface trusted information across a distributed data estate.  Whether data is located in a data lake, in the cloud, or in relational data stores, we make it easy to: •  Search for data using common business terms • 

The Intelligent Data Lake

Today we are announcing the general availability of Azure Data Lake, ushering in a new era of productivity for your big data developers and scientists.

Our state-of-the-art development environment and rich and extensible U-SQL language enable you to write, debug, and optimize massively parallel analytics programs in a fraction of the time of existing solutions.

Traditional approaches for big data analytics constrain the productivity of your data developers and scientists due to time spent on infrastructure planning, and writing, debugging, &

They also lack rich built-in cognitive capabilities like keyphrase extraction, sentiment analysis, image tagging, OCR, face detection, and emotion analysis.

It can manage trillions of files where a single file can be greater than a petabyte in size - this is 200x larger file size than other cloud object stores.

“DS-IQ provides Dynamic Shopper Intelligence by curating data from large amounts of non-relational sources like weather, health, traffic, and economic trends so that we can give our customers actionable insights to drive the most effective marketing and service communications.

This scalability and performance has impressed us, giving us confidence that it can handle the amounts of data we need to process today and, in the future, enable us to provide even more valuable, dynamic, context-aware experiences for our clients.”

-William Wu, Chief Technology Officer at DS-IQ Data Lake Store provides massive throughput to run analytic jobs with thousands of concurrent executors that read and write hundreds of terabytes of data efficiently.

“Azure Data Lake has been deployed to our water division where we are collecting real-time data from IoT devices so we can help our customers understand how they can reduce, reuse, and recycle water and at the same address one of the world’s most pressing sustainability issues.

Only pay for the processing used per job, freeing valuable developer time from doing capacity planning and optimizations required in cluster-based systems that can take weeks to months.

“Azure Data Lake is instrumental because it helps Insightcentr ingest IoT-scale telemetry from PCs in real-time and gives detailed analytics to our customers without us spending millions of dollars building out big data clusters by scratch.

Instead of writing low-level code dealing with clusters, nodes, mappers, and reducers, etc., a developer writes a simple logical description of how data should be transformed for their business using both declarative and imperative techniques as desired.

You can massively parallelize the code to process petabytes of data for diverse workload categories such as ETL, machine learning, feature engineering, image tagging, emotion detection, face detection, deriving meaning from text, and sentiment analysis.

Now you can process any amount of unstructured data, e.g., text, images, and extract emotions, age, and all sorts of other cognitive features using Azure Data Lake and perform query by content.

It’s not just extracting one piece of cognitive information at a time, not just about understanding an emotion or whether there’s an object in an image, but rather it’s about joining all the extracted cognitive data with other types of data, so you can do some really powerful analytics with it.

Azure Data Lake makes debugging failures in cloud distributed programs as easy as debugging a program in your personal environment using the powerful tools within Visual Studio.

Our service can detect and analyze common performance problems that big data developers encounter such as imbalanced data partitioning and offers suggestions to fix your programs using the intelligence we’ve gathered in the analysis of over a billion jobs in Microsoft’s data lake.

Developers must manually optimize their data transformations, requiring them to carefully investigate how their data is transformed step-by-step, often manually ordering steps to gain improvements.

We are now taking this a step further and exposing the powerful Data Lake tools directly to our clients in our software allowing them to more easily explore their data using these tools.”

This service lets you ingest massive amounts of real-time data and analyze that data with integration to Storm, Spark, for HDInsight and Azure IoT Hub to build end-to-end IoT, fraud detection, click-stream analysis, financial alerts, or social analytics solutions.

Multi-threaded math libraries and transparent parallelization in R Server enables handling up to 1000x more data and up to 50x faster speeds than open source R—helping you train more accurate models for better predictions than previously possible.

They are built with the highest levels of security for authentication, authorization, auditing, and encryption to give you peace-of-mind when storing and analyzing sensitive corporate data and intellectual property.

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