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 4 months ago

Public Feedback

comment Suggest improvements to the Speaker
  • rachana oza
    By rachana oza  ~  3 months ago
    reply Reply

    Applications of deep learning in healthcare is an emerging research area. Hope in this session, we will surely gain future directions for research in healthcare using deep learning/machine learning.

     

    • Dr. Mayuri Mehta
      By Dr. Mayuri Mehta  ~  3 months ago
      reply Reply

      Yes Ms. Rachana, as mentioned in the outline of session, future scope will be discussed at the end of session. 

  • Hemen Trivedi
    By Hemen Trivedi  ~  3 months ago
    reply Reply

    Hello Dr. Mayuri

    Can the healthcare applications, which you are planning to demonstrate be implemented using other tools and technologies?

    • Dr. Mayuri Mehta
      By Dr. Mayuri Mehta  ~  3 months ago
      reply Reply

      Hello Mr. Hemen Trivedi,

      I have developed healthcare applications using TensorFlow because I prefer open source technology. However, the same can be developed with other Python libraries such as Keras, Theano, Torch, etc. as well as with licensed software such as Matlab, etc. We are developing our new healthcare applications using Keras.

      Thank you.

  • Dr. Om Deshmukh
    By Dr. Om Deshmukh  ~  3 months ago
    reply Reply

    The three applications mentioned are quite interesting and relevant in the Indian setup. 

    What will help is to have some technical details on what specific challenges were faced, how they were solved and what are the learnings for the broader community. 

    • Dr. Mayuri Mehta
      By Dr. Mayuri Mehta  ~  3 months ago
      reply Reply

      Dear Dr. Om Deshmukh,

      Thank you,

      During demonstration, I will surely discuss the challenges faced and how they were solved.

      • Naresh Jain
        By Naresh Jain  ~  3 months ago
        reply Reply

        Hi Dr. Mayuri,

        Request you to please update all these details in your proposal itself now to help the program committee evaluate your proposal.

        • Dr. Mayuri Mehta
          By Dr. Mayuri Mehta  ~  3 months ago
          reply Reply

          Hello Mr. Naresh,

          The proposal is updated as per the suggestions received.

          Thank you.

  • Dipanjan Sarkar
    By Dipanjan Sarkar  ~  3 months ago
    reply Reply

    The topic is very good, my only concern is do you think 45 minutes would be enough for the hands-on based demonstrations you are planning or do you think you might need more time?

    • Dr. Mayuri Mehta
      By Dr. Mayuri Mehta  ~  3 months ago
      reply Reply
      Hello Dipanjan,
       
      I appreciate your concern. I agree that it would have been better to get more time for my demonstration. However, considering the time limit for demonstration session and considering the profile of the probable attendees (having basic background of deep learning), I have worked out my session as to-the-point and am sure will be able to complete it within the given time limit. 
       
      Thank you.
       

       

  • shanselvi D
    By shanselvi D  ~  3 months ago
    reply Reply

    Hai madam,

        Congrats for submitting the proposal in the emerging field.   This will bring out more solutions by the budding reasearchers. More hands on session will give better understanding of the concept. I appreciate for recommending tensorflow open source software. All the best.

     

     

     

     

    • Dr. Mayuri Mehta
      By Dr. Mayuri Mehta  ~  3 months ago
      reply Reply

      Hello Madam,

      Thank you very much.

  • Nirav Mehta
    By Nirav Mehta  ~  4 months ago
    reply Reply

    Hi Mayuri,

    Could you please let us know that the applications which you are goibg to demonstrate are developed or will be demonstrated  using which tools and technology?

    • Dr. Mayuri Mehta
      By Dr. Mayuri Mehta  ~  4 months ago
      reply Reply

      Hello Mr. Nirav,

      So far we have proposed three AI based healthcare solutions using TensorFlow (TensorFlow is an open source machine learning library). We are also working on some more healthcare projects using TensorFlow. Hence, the healthcare applications will be demonstrated using TensorFlow during the session.

       

      • Dr. Vikas Agrawal
        By Dr. Vikas Agrawal  ~  4 months ago
        reply Reply

        Dear Dr. Mayuri: Would you like to specify those healthcare solutions i.e. what specific business problem is being solved, how are you solving it and what is the success relative to academic/industry benchmarks? That will significantly enrich our understanding of the talk. 

        Warm Regards

        Vikas

        • Dr. Mayuri Mehta
          By Dr. Mayuri Mehta  ~  4 months ago
          reply Reply

          Hello Dr. Vikas,

          To make the session informative, educational, benefitial and valuable, I will certainly demonstrate each healthcare solution including problem statement, proposed solution and performance analysis.

          For each of our healthcare projects, we could not find ready dataset and hence, we collected a good amount of data consulting several doctors to perform exhaustive empirical analysis and to verify the success of proposed solution.

  • By  ~  4 months ago
    reply Reply

    Hi

    Use of AI in Healthcare is emerging. Would like to know if you will be discussing future research directions?

    • Dr. Mayuri Mehta
      By Dr. Mayuri Mehta  ~  4 months ago
      reply Reply

      Hello,

      At the end of session, I shall surely discuss the scope of research in our healthcare solutions that are to be demonstrated. Moreover, I shall discuss other potentially fruitful areas of research in healthcare  that have been emerged from our study and are identified.


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    The theme will be updated soon .

    Our celebrity and distinguished presenter Srikanth Ramaswamy who is an advisor at Mysuru Consulting Group and also works Blue Brain Project at the EPFL will be delivering an expert talk in the workshop.

    https://www.linkedin.com/in/ramaswamysrikanth/

    { This workshop will be a combination of panel discussions , expert talk and neuroimaging data science workshop ( applying machine learning and deep learning algorithms to Neuroimaging data sets}

    { We are currently onboarding several experts from Neuroscience domain --Neurosurgeons , Neuroscientists and Computational Neuroscientists .Details of the speakers will be released soon }

    Abstract for the Neuroimaging Data Science Part of the workshop:

    The study of the human brain with neuroimaging technologies is at the cusp of an exciting era of Big Data. Many data collection projects, such as the NIH-funded Human Connectome Project, have made large, high- quality datasets of human neuroimaging data freely available to researchers. These large data sets promise to provide important new insights about human brain structure and function, and to provide us the clues needed to address a variety of neurological and psychiatric disorders. However, neuroscience researchers still face substantial challenges in capitalizing on these data, because these Big Data require a different set of technical and theoretical tools than those that are required for analyzing traditional experimental data. These skills and ideas, collectively referred to as Data Science, include knowledge in computer science and software engineering, databases, machine learning and statistics, and data visualization.

    The workshop covers Data analysis, statistics and data visualization and applying cutting-edge analytics to complex and multimodal neuroimaging datasets . Topics which will be covered in this workshop are statistics, associative techniques, graph theoretical analysis, causal models, nonparametric inference, and meta-analytical synthesis.

  • Liked Raunak Bhandari
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    Raunak Bhandari / Ankit Desai / Usha Rengaraju - Knowledge Graph from Natural Language: Incorporating order from textual chaos

    90 Mins
    Workshop
    Advanced

    Intro

    What If I told you that instead of the age-old saying that "a picture is worth a thousand words", it could be that "a word is worth a thousand pictures"?

    Language evolved as an abstraction of distilled information observed and collected from the environment for sophisticated and efficient interpersonal communication and is responsible for humanity's ability to collaborate by storing and sharing experiences. Words represent evocative abstractions over information encoded in our memory and are a composition of many primitive information types.

    That is why language processing is a much more challenging domain and witnessed a delayed 'imagenet' moment.

    One of the cornerstone applications of natural language processing is to leverage the language's inherent structural properties to build a knowledge graph of the world.

    Knowledge Graphs

    Knowledge graph is a form of a rich knowledge base which represents information as an interconnected web of entities and their interactions with each other. This naturally manifests as a graph data structure, where nodes represent entities and the relationship between them are the edges.

    Automatically constructing and leveraging it in an intelligent system is an AI-hard problem, and an amalgamation of a wide variety of fields like natural language processing, information extraction and retrieval, graph algorithms, deep learning, etc.

    It represents a paradigm shift for artificial intelligence systems by going beyond deep learning driven pattern recognition and towards more sophisticated forms of intelligence rooted in reasoning to solve much more complicated tasks.

    To elucidate the differences between reasoning and pattern recognition: consider the problem of computer vision: the vision stack processes an image to detect shapes and patterns in order to identify objects - this is pattern recognition, whereas reasoning is much more complex - to associate detected objects with each other in order to meaningfully describe a scene. For this to be accomplished, a system needs to have a rich understanding of the entities within the scene and their relationships with each other.

    To understand a scene where a person is drinking a can of cola, a system needs to understand concepts like people, that they drink certain liquids via their mouths, liquids can be placed into metallic containers which can be held within a palm to be consumed, and the generational phenomenon that is cola, among others. A sophisticated vision system can then use this rich understanding to fetch details about cola in-order to alert the user of their calorie intake, or to update preferences for a customer. A Knowledge Graph's 'awareness' of the world phenomenons can thus be used to augment a vision system to facilitate such higher order semantic reasoning.

    In production systems though, reasoning may be cast into a pattern recognition problem by limiting the scope of the system for feasibility, but this may be insufficient as the complexity of the system scales or we try to solve general intelligence.

    Challenges in building a Knowledge Graph

    There are two primary challenges towards integrating knowledge graphs in systems: acquisition of knowledge and construction of the graph and effectively leveraging it with robust algorithms to solve reasoning tasks. Creation of the knowledge graph can vary widely depending on the breadth and complexity of the domain - from just manual curation to automatically constructing it by leveraging unstructured/semi-structured sources of knowledge, like books and Wikipedia.

    Many natural language processing tasks are precursors towards building knowledge graphs from unstructured text, like syntactic parsing, information extraction, entity linking, named entity recognition, relationship extraction, semantic parsing, semantic role labeling, entity disambiguation, etc. Open information extraction is an active area of research on extracting semantic triplets of object ('John'), predicate ('eats'), subject ('burger') from plain text, which are used to build the knowledge graph automatically.

    A very interesting approach to this problem is the extraction of frame semantics. Frame semantics relates linguistic semantics to encyclopedic knowledge and the basic idea is that the meaning of a word is linked to all essential knowledge that relates to it, for eg. to understand the word "sell", it's necessary to also know about commercial transactions, which involve a seller, buyer, goods, payment, and the relations between these, which can be represented in a knowledge graph.

    This workshop will focus on building such a knowledge graph from unstructured text.

    Learn good research practices like organizing code and modularizing output for productive data wrangling to improve algorithm performance.

    Knowledge Graph at Embibe

    We will showcase how Embibe's proprietary Knowledge Graph manifests and how it's leveraged across a multitude of projects in our Data Science Lab.

  • Liked Maryam Jahanshahi
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    Maryam Jahanshahi - Applying Dynamic Embeddings in Natural Language Processing to Analyze Text over Time

    Maryam Jahanshahi
    Maryam Jahanshahi
    Research Scientist
    TapRecruit
    schedule 7 months ago
    Sold Out!
    45 Mins
    Case Study
    Intermediate

    Many data scientists are familiar with word embedding models such as word2vec, which capture semantic similarity of words in a large corpus. However, word embeddings are limited in their ability to interrogate a corpus alongside other context or over time. Moreover, word embedding models either need significant amounts of data, or tuning through transfer learning of a domain-specific vocabulary that is unique to most commercial applications.

    In this talk, I will introduce exponential family embeddings. Developed by Rudolph and Blei, these methods extend the idea of word embeddings to other types of high-dimensional data. I will demonstrate how they can be used to conduct advanced topic modeling on datasets that are medium-sized, which are specialized enough to require significant modifications of a word2vec model and contain more general data types (including categorical, count, continuous). I will discuss how my team implemented a dynamic embedding model using Tensor Flow and our proprietary corpus of job descriptions. Using both categorical and natural language data associated with jobs, we charted the development of different skill sets over the last 3 years. I will specifically focus the description of results on how tech and data science skill sets have developed, grown and pollinated other types of jobs over time.

  • Liked Saurabh Jha
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    Saurabh Jha / Rohan Shravan / Usha Rengaraju - Hands on Deep Learning for Computer Vision

    480 Mins
    Workshop
    Intermediate

    Computer Vision has lots of applications including medical imaging, autonomous
    vehicles, industrial inspection and augmented reality. Use of Deep Learning for
    computer Vision can be categorized into multiple categories for both images and
    videos – Classification, detection, segmentation & generation.
    Having worked in Deep Learning with a focus on Computer Vision have come
    across various challenges and learned best practices over a period
    experimenting with cutting edge ideas. This workshop is for Data Scientists &
    Computer Vision Engineers whose focus is deep learning. We will cover state of
    the art architectures for Image Classification, Segmentation and practical tips &
    tricks to train a deep neural network models. It will be hands on session where
    every concepts will be introduced through python code and our choice of deep
    learning framework will be PyTorch v1.0 and Keras.

    Given we have only 8 hours, we will cover the most important fundamentals,
    current techniques and avoid anything which is obsolete or not being used by
    state-of-art algorithms. We will directly start with building the intuition for
    Convolutional Neural Networks, and focus on core architectural problems. We
    will try and answer some of the hard questions like how many layers must be
    there in a network, how many kernels should we add. We will look at the
    architectural journey of some of the best papers and discover what each brought
    into the field of Vision AI, making today’s best networks possible. We will cover 9
    different kinds of Convolutions which will cover a spectrum of problems like
    running DNNs on constrained hardware, super-resolution, image segmentation,
    etc. The concepts would be good enough for all of us to move to harder problems
    like segmentation or super-resolution later, but we will focus on object
    recognition, followed by object detections. We will build our networks step by
    step, learning how optimizations techniques actually improve our networks and
    exactly when should we introduce them. We hope the leave you in confidence
    which will help you read research papers like your second nature. Given we have
    8 hours, and we want the sessions to be productive, we will instead of introducing

    all the problems and solutions, focus on the fundamentals of modern deep neural
    networks.

  • Liked Anant Jain
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    Anant Jain - Adversarial Attacks on Neural Networks

    Anant Jain
    Anant Jain
    Co-Founder
    Compose Labs, Inc.
    schedule 6 months ago
    Sold Out!
    20 Mins
    Talk
    Intermediate

    Since 2014, adversarial examples in Deep Neural Networks have come a long way. This talk aims to be a comprehensive introduction to adversarial attacks including various threat models (black box/white box), approaches to create adversarial examples and will include demos. The talk will dive deep into the intuition behind why adversarial examples exhibit the properties they do — in particular, transferability across models and training data, as well as high confidence of incorrect labels. Finally, we will go over various approaches to mitigate these attacks (Adversarial Training, Defensive Distillation, Gradient Masking, etc.) and discuss what seems to have worked best over the past year.

  • Liked Amit  Baldwa
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    Amit Baldwa - PREDICTING AND BEATING THE STOCK MARKET WITH MACHINE LEARNING AND TECHNICAL ANALYSIS

    Amit  Baldwa
    Amit Baldwa
    Director
    Finastra Financial Software
    schedule 4 months ago
    Sold Out!
    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