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Google’s Deep Learning Tool Now Increases Accuracy for Breast Cancer Detection

Google’s Deep Learning Tool Now Increases Accuracy for Breast Cancer Detection

Google has finally developed a deep learning tool that identifies breast cancer that has spread to lymph nodes in pathology slides with 99% accuracy. It would surely reduce the average slide review time.

Detecting how far cancer has spread within a patient’s body is a Herculean task. Especially, for breast cancer. In this case, we’ve to detect how far cancer has spread from a primary region to neighboring lymph nodes. Nodal metastasis is the key here. It influences observations circulating radiation and chemotherapy, resulting in timely and proper detection.

Nevertheless, clinicians have always struggled to determine correctly how far the disease has spread. Fortunately, Google’s AI team proved better and productive at determining metastatic breast cancer with a greater accuracy. Two research papers by Google AI team have implemented deep learning methods to address the consequential challenge, and have lent a helping hand to the pathologists for effectively detecting breast cancer.

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An algorithm, known as LYNA, Lymph Node Assistant has been developed to identify the regions of tumors that have spread or metastasized. Till now, they were extremely difficult to be detected by normal clinicians. As a well-known fact, out of half a million deaths across the globe owing to breast cancer, more than 90% are as a result of metastasis.

The abovementioned technology from Google first appeared in 2017. According to a recent publication, the AI research team at Google was influenced by “gigapixel-sized pathology slides of lymph nodes from breast cancer patients” for curating such an advanced algorithm. Moreover, the blog post revealed that the system was also able to “accurately pinpoint the location of both cancers and other suspicious regions within each slide.” In some cases, the locations are so minute that pathologists may have a hard time trying to detect them accurately.

The best part about LYNA system is regarding the area of concern for clinicians, doctors and how to enhance the entire process of review and ultimate diagnosis. According to Google, the underlying principle of this technology is to help doctors detecting metastatic breast cancer instead of replacing the human workforce. Thanks to the study and of course LYNA, the pathologists are in a better shape to accurately detect the micrometastases.

“Pathologists with LYNA assistance were more accurate than either unassisted pathologists or the LYNA algorithm itself,” reveals the blog post. This means the algorithms will become more productive when implemented by people, rather than working on their own.

However, the robust deep learning technology in question here does have some limitations – it works for limited dataset sizes. Further, only a single lymph node was scrutinized for every patient rather than multiple slides that would be common for a comprehensive clinical case. Thus, more detailed work needs to be done on LYNA before being applied to real-life patient situations.

For a detailed report, study “Artificial Intelligence Based Breast Cancer Nodal Metastasis Detection: Insights into the Black Box for Pathologists” as well as “Impact of Deep Learning Assistance on the Histopathologic Review of Lymph Nodes for Metastatic Breast Cancer.”

To know more about deep learning and how machine learning fuels the state of the art technology of deep learning, enroll in Deep Learning Training in Gurgaon. DexLab Analytics is one of the well-recognized deep learning training institutes in Delhi that offers in-demand skill training courses. For more information, visit their official site now.

 

The blog has been sourced from — indianexpress.com/article/technology/science/google-new-deep-learning-algorithm-could-improve-detection-of-breast-cancer-5412456

 

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4 FAQs on Deep Learning

4 FAQs on Deep Learning

AI is revolutionizing the world. And Deep Learning is right in the heart of it. Yes, Deep Learning is a paradigm of machine learning, where algorithms are developed in a manner resembling the structure and function of human brain, and the entire phenomenon is called Artificial Neural Network.

From Facebook’s research to Netflix’s movie recommendations to DeepMind’s iconic algorithms, Deep Learning has indeed come a long way. Legendary innovations, awe-inspiring breakthroughs and latest technologies added to the flight. So, now that it became one of the hottest trends in the IT industry, you might be wondering what exactly in this nuanced AI concept… or how much does it includes studying mathematics and statistics… or how much deep is Deep Learning…

To answer all your questions and introduce you to the intricacies of such an in-demand IT skill, we’re here – this blog should help you in your quest for knowledge on Deep Learning.

Let’s get started!

What is Deep Learning and what makes it so popular?

Deep Learning is a significant part of AI that involves imitating the way a human brain functions, while learning some kind of knowledge. Put simply, this new branch of science has a lot to do with automating predictive analytics.

The superiority of human brain is unbeatable; this is why Deep Learning models is considered to be the most versatile and self-efficient man-made models ever been created. Using such an eccentric model, deriving crucial information from a humongous amount of data is what makes Deep Learning so special, and of course popular.

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What are some of its real-life applications?

  • Facebook and Google are translating text into various different languages, all at the same time.
  • Siri, Cortana, Alexa are effortless working towards simplifying speech recognition techniques – their voice commands have ignited a whole new world of possibilities for a machine.
  • Deep Learning is increasingly influencing impactful computer vision applications, including OCR (Optical Character Recognition) and real time language translation.
  • Snapchat and Instagram use facial feature detection – which involves a larger chunk of Deep Learning technology.
  • In the healthcare domain, detection of malignant cells has been possible because of this latest technology.

What are the prerequisites to get started with Deep Learning?

 Starting a career in this hottest field of science is not as difficult as it sounds to be. Deep Learning requires you to possess some knowledge on the following fields of study:

  • Mathematics
  • Statistics
  • Machine Learning
  • Basic skills for Coding

Which tools to possess to ace Deep Learning?

Hailing from the field of data science, I would always recommend Python certification course – because it is simple, robust, efficient, and has its own open source libraries and supports a large, active community of users. That being said, Python is a universal programming language; it can be used for development as well as implementation.

Besides Python, aspiring newbies are free to grasp top notch libraries, such as Keras. It simplifies the experimentation task and ensures access to the parameters that amplified the performance of similar models.

DexLab Analytics is a leading Python training institute in Delhi; if you are interested, you can browse through their course itinerary and make a well-informed decision.

 
The blog has been sourced from —

www.analyticsvidhya.com/blog/2018/05/deep-learning-faq

searchenterpriseai.techtarget.com/definition/deep-learning-deep-neural-network
 

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Deep Learning: A Comprehensive Study

Deep Learning: A Comprehensive Study

Deep Learning is a subdivision of machine learning, under the category of artificial intelligence. It’s based on a fixed set of algorithms that strives to model advanced level abstractions in data. In a simple model, you would be having two sets of neurons, where if the input layer receives any input, it transmits a revamped version of input to the next layer. However, in a deep network, there exists a web of many layers between input and output, compelling the algorithm to rely on multiple processing layers, made of numerous layers and non-linear transformations.

No wonder, Deep Learning has triggered a revolution in the machine learning realm. Interesting works are being carried on in this field. Innovative technology is modifying speech recognition, object detection, visual object recognition and other sectors, like genomics and drug discovery. And, yes, we are excited about all the new good things that’s happening around!!

For more detailed analysis, scroll below:

About Deep Learning Architecture

  • Generative deep architectures are created to characterize high-order correlation attributes of visible data for all sorts of pattern analysis as well as synthetic purposes.
  • Discriminative deep architectures are specialized in offering discriminative power for pattern classification, mostly by showcasing posterior distribution of classes subject to visible data.
  • Hybrid deep architectures are designed for discrimination but are aided with results of generative architectures through better optimization as well as regularization.

A Few Applications of Deep Learning

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Colorization of BW Images

Deep learning has the ability to recreate an image with the addition of color. The cutting edge technology uses the objects and the entire context within a picture for coloring the whole image, quite similar to a human approach. For this, extensive supervised layers and convulational neural network have to be put to use, of course.

Generative Model Chatbots

They are in hype. A sequence-to-sequence model is widely used to design chatbots which are capable of generating their own answer when trained on a wide set of real-live interactive datasets.

Machine Translations

Text translation is very easy to perform without following any proper sequence, allowing algorithms to ace dependencies between words and plotting to a new language.

Automatic Game Playing

Here, a model is trained to play a computer game formulated on the pixels on the screen. The task is fairly challenging and is one of the most fascinating domains of deep reinforcement models, Deep Mind.

Automatic Handwriting Generation

Here, you have to generate a new handwriting for a particular word or phrase using this technology. The handwritting is given as a sequence of coordinates written by a pen once the samples are done.

As parting thoughts, Deep Learning is still in a nascent stage in India. But, its diverse uses and capabilities will surely put it in the industry frontline some day soon. So, if you are looking for good deep learning training courses in Gurgaon, DexLab Analytics offers some out of the box kind of learning experience. Do check out their deep learning certification courses, they are excellent!

 

The blog has been sourced from — medium.com/@shridhar743/a-beginners-guide-to-deep-learning-5ee814cf7706

www.zdnet.com/article/what-is-deep-learning-everything-you-need-to-know
 

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