Building Deep Learning based Healthcare Application using TensorFlow
Machine learning and deep learning have been rapidly adopted in various spheres of medicine such as discovery of drug, disease diagnosis, Genomics, medical imaging and bioinformatics for translating biomedical data into improved human healthcare. Machine learning/deep learning based healthcare applications assist physicians to make faster, cheaper and more accurate diagnosis.
We have successfully developed three deep learning based healthcare applications and are currently working on two more healthcare related projects. In this workshop, we will discuss one healthcare application titled "Deep Learning based Craniofacial Distance Measurement for Facial Reconstructive Surgery" which is developed by us using TensorFlow. Craniofacial distances play important role in providing information related to facial structure. They include measurements of head and face which are to be measured from image. They are used in facial reconstructive surgeries such as cephalometry, treatment planning of various malocclusions, craniofacial anomalies, facial contouring, facial rejuvenation and different forehead surgeries in which reliable and accurate data are very important and cannot be compromised.
Our discussion on healthcare application will include precise problem statement, the major steps involved in the solution (deep learning based face detection & facial landmarking and craniofacial distance measurement), data set, experimental analysis and challenges faced & overcame to achieve this success. Subsequently, we will provide hands-on exposure to implement this healthcare solution using TensorFlow. Finally, we will briefly discuss the possible extensions of our work and the future scope of research in healthcare sector.
Outline/Structure of the Workshop
- Significance of Deep Learning for Healthcare Solutions (10 mins)
- Discussion of Healthcare Application - 'Deep Learning based Craniofacial Distance Measurement for Facial Reconstructive Surgery' (20 mins)
- Craniofacial distances and their application in facial reconstructive surgeries (5 mins)
- Issues in conventional method of measuring craniofacial distances (3 mins)
- Problem statement (2 mins)
- Proposed solution (5 mins)
- Introduction to TensorFlow components and program structure (5 mins)
- Hands-on Healthcare Application (Craniofacial Distance Measurement for Facial Reconstructive Surgery) using TensorFlow (50 mins)
- Getting started with Google Colaboratory (5 mins)
- Practice sample programs using TensorFlow in Google Colaboratory (10 min)
- Explanation of dataset (5 min)
- Justification for Python libraries/packages used (5 min)
- Implementation of healthcare application using pretrained CNN (10 min)
- Implementation of healthcare application using CE_CLM model (10 min)
- Results and discussion (5 mins)
- Future Research Directions (5 mins)
- Q & A (5 mins)
After attending this workshop, participants will get an overview of TensorFlow and how to build machine learning/deep learning based application using TensorFlow.
Data Scientists, Machine Learning/Deep Learning Practitioners, Python Programmers, Doctors, Researchers, Students & Faculty Members from sectors such as Engineering and Technology, Medical
Prerequisites for Attendees
Familiarity with fundamentals of machine learning and deep learning.
Basic knowledge of Python programming.
- Download files from https://drive.google.com/drive/folders/17fvlAkyoJ5cLmd5XJk-dz6A5Eor6zhpr?usp=sharing and upload all these files in the folder named 'ODSC2019' in your Google drive.
Upload all these files in the folder named 'ODSC2019' in your Google drive.
schedule Submitted 7 months ago
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