Recent advancements in AI are proving beneficial in development of applications in various spheres of healthcare sector such as microbiological analysis, discovery of drug, disease diagnosis, Genomics, medical imaging and bioinformatics for translating a large-scale data into improved human healthcare. Automation in healthcare using machine learning/deep learning assists physicians to make faster, cheaper and more accurate diagnoses.

Due to increasing availability of electronic healthcare data (structured as well as unstructured data) and rapid progress of analytics techniques, a lot of research is being carried out in this area. Popular AI techniques include machine learning/deep learning for structured data and natural language processing for unstructured data. Guided by relevant clinical questions, powerful deep learning techniques can unlock clinically relevant information hidden in the massive amount of data, which in turn can assist clinical decision making.

We have successfully developed three deep learning based healthcare applications using TensorFlow and are currently working on three more healthcare related projects. In this demonstration session, first we shall briefly discuss the significance of deep learning for healthcare solutions. Next, we will demonstrate two deep learning based healthcare applications developed by us. The discussion of each application will include precise problem statement, proposed solution, data collected & used, experimental analysis and challenges encountered & overcame to achieve this success. Finally, we will briefly discuss the other applications on which we are currently working and the future scope of research in this area.

 
 

Outline/Structure of the Demonstration

  • Significance of Deep Learning for Healthcare Applications (5 mins)
  • Demonstration of Healthcare Applications (30 mins)
    1. A Deep Learning based Automated Approach to Detect Dry Eye Disease
    2. Deep Learning based Craniofacial Distance Measurement for Facial Reconstructive Surgery
  • Future Research Directions (5 mins)
  • Q & A (5 mins)

Learning Outcome

After attending this session, attendees will be able to…

  • Understand AI-powered healthcare solutions/applications.
  • Extend use of deep learning to create healthcare solutions.
  • Identify different and varied opportunities in healthcare sector where deep learning can be applied for better healthcare solution.

Target Audience

Students, faculty members and researchers from sectors such as Engineering and Technology, Medical and Industry. Doctors should attend this session to understand technological advancements in healthcare sector.

Prerequisites for Attendees

  • Familiarity with fundamentals of machine Learning

schedule Submitted 1 year ago

Public Feedback


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      schedule 1 year ago
      Sold Out!
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      Anant Jain - Adversarial Attacks on Neural Networks

      Anant Jain
      Anant Jain
      Co-Founder
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      schedule 1 year ago
      Sold Out!
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    • Liked Amit  Baldwa
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      Amit Baldwa - PREDICTING AND BEATING THE STOCK MARKET WITH MACHINE LEARNING AND TECHNICAL ANALYSIS

      45 Mins
      Demonstration
      Intermediate

      Machine learning provides systems the ability to automatically learn and improve from experience without being explicitly programmed.

      Technical analysis shows in graphic form investor sentiment, both greed and fear. Technical analysis attempts to use past stock price and volume information to predict future price movements. Technical analysis of various indicators has been a time-tested strategy for seasoned traders and hedge funds, who have used these techniques to effective turn our profits in Securities Industry.

      Some researchers claim that stock prices conform to the theory of random walk, which is that the future path of the price of a stock is not more predictable than random numbers. However, Stock prices do not follow random walks.

      We will evaluate whether stock returns can be predicted based on historical information.

      Coupled with Machine Learning, we further try to decipher the correlation between the various indicators and identify the set of indicators which appropriately predict the value