Category: deep learning course

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.

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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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

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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