Automated analysis of CT scan images using AI solutions to diagnose abnormalities will help in overcoming the costly, time consuming and prone to error from manual analysis. Deep Learning has proved to be quite efficient to mimic human cognitive abilities (and even exceed that in many cases), especially with unstructured data.

DL algorithms can detect, localize and quantify a growing list of brain pathologies including intra-cerebral bleeds and their subtypes, infarcts, mass effect, midline shift, and cranial fractures. So, with advanced DL algorithms, analysis of radiographic data can be easily achieved and this can accelerate early detection of certain critical medical conditions, powered by AI.

As mentioned, Deep Learning algorithms for computer vision use cases has been extremely successful for classification and localization related problems. With the availability of annotated dataset, object of interest or region of interest segmentation using Deep Learning has been plausible.

Algorithms like Regional Convolutional Neural Network (RCNN) and it’s evolved forms, Faster RCNN and Masked RCNN is being widely used in the field of advanced radiology to auto detect medical conditions through radio-graphic images.

For this session, I am particularly going to talk about application of Masked RCNN for detection of regions of brain haemorrhage from CT scan images of the brain.

Advanced deep learning approaches have been quite successful to solve real world problems related to unstructured data like images, audio-signals and texts. But how often are we able to make best use of these solutions by exercising their benefits at scale? Even when the deep neural network based solutions have made a significant impact in the world of AI and data science, but the key challenge which most organizations face, is bringing their solutions to production. As a matter of fact, until and unless these remarkable but yet complex algorithms are operated at scale, considering production and if these doesn’t have the capability to be integrated with various software applications, these solutions will never be really impactful.

So, for this talk, I will be discussing about various approaches to accelerate deep learning solutions from notebooks or research environment to production environment and how these solutions can be transformed as an enterprise level end to end Deep Learning Solution, which can be consumed as a service by any software application, with a practical use-case example.

Automated analysis of Chest X-ray images to diagnose various pathologies will help in overcoming the costly, time consuming and prone to error from manual analysis of them, especially using deep learning based approaches. One of such recent efforts in this direction is Classification of Common Thorax which combines the advantages of CNN based feature extraction and problem transformation methods in multi-label classification task.

So this is one of the key areas where deep learning based solution has already made an impact and has the potential to come up with even a better and well improved performance.

For this session, I am going to discuss about the problem at hand, the data-set, several approaches that has been explored and that worked quite well so far in this research. Also I am going to mention about the potential use case and the real world impact of such a real world healthcare application that can save millions of lives by early and effective detection.

Also I am going to mention about some of the key challenges faced during this research and how it can be scaled to build an end to end software solution!

Workshop: Introduction to Image Generation in Computer Vision using Deep Learning

Impact of Data Science and Artificial Intelligence on Societal Growth

Introduction :

"Special children" includes children who are affected with a complex neuro-behavioral conditions like autism, which includes impairments in social interaction, language development and communication skills, combined with rigid, repetitive behaviors. Children with autism particularly face a very difficult childhood as they have extreme difficulty in communication. They have trouble in understanding what other people think and feel. This makes it very hard for them to express themselves either with words or through gestures.

Such special children need “special” care for the development of their cognitive abilities. The amount of learning resources required for teaching such children are extremely hard to find and less accessible to many.

So, can artificial intelligence with the help of modern deep learning algorithms generate animated videos for developing or improving cognitive abilities of such a special group?

The idea to combat the problem:

Well, I feel it can be done!

An animated video consists of 3 main components:

1. Graphical video (sequence of images put together to tell a story),

2. A background story and

3. A relevant background audio or music.

Now if we have to come up with a system that produces machine generated animated video, we would have to think about these three components:

1. Machine generated sequence of images with a spatial coherence
2. Machine generated text, or the story
3. Machine generated audio or music, that highlights the mood or the theme of the video

If these three discrete components are put together in a cohesive flow, our purpose can be achieved. And the Deep Learning community has already been able to make significant progress in terms of machine generated images and audio and machine generated text.

Details about the three pillars of this problem:

Machine generated sequence of images with a spatial coherence

Generative Adversarial Networks (GANs) has been quite successful till date to come up with generated images and audio. Also, for our use case, to maintain a coherency in spatial features, Variational Auto Encoders (VAEs) have been even better.

If we start with a popular use case of a very popular cartoon series, Tom & Jerry, specially modified for autistic children, let’s consider a simple scene where tom is chasing jerry. On an image level, for the entire scene, the posture of tom and jerry will remain constant, only their location will vary in every subsequent image frame in the entire scene. Which means, only their spatial location with respect to the entire image background will vary and hence VAEs will have the potential to implement such a use case as VAEs helps to provide probabilistic descriptions of features or observations in latent spaces.

Machine generated text, or the story

Coming to text generation or story generation, recurrent neural networks like Long/Short Term Memory (LSTM) has been quite successful. Already, LSTM has been used to artificially generate chapters from popular novels or stories like Harry Potter and Cinderella. So, for a simple animated video story specially structured for autistic children, LSTM can be effective. Although Gradient Recurrent Units (GRU) can be the other alternative, but till date LSTM has been more successful, so the first preference will always be LSTM.

Machine generated audio or music

For music generation, GANs have been proved effective till date. For our use case, Natural Language Processing or NLP can used to determine the type of scene from the generated story, e.g. for the Tom & Jerry scene, it will be a chase scene. Based on this classification, Deep Convolution Generative Adversarial Networks (DCGAN) can be used to generate music which is relevant to such a chase scene and at the same time be soothing and enjoyable to such children!

Assembling everything together

Now if we can put all these discrete pieces of the puzzle together, we can come up with a completely machine generated animated video tailor-made for developing and improving cognitive abilities of children with autism. This will be a new progress in the field of Artificial Intelligence!

These machine generated videos can be trained on Neural Network in such a way that it can be a source of fun and enjoyment for this special group and at the same time reward their good behavior and educate them in a sensitive way without any human dependency.

Future scope and extension

As a future scope, if this approach is successful, the gaming industry can adopt usage of such a technology and with the help of reinforcement learning, can come up with machine generated video games and educational games specially designed for such children that can disrupt the entire gaming industry and can be a source of happiness for such children!