Get updates on Eligibility, Admission, Placements Fees Structure
Quick Facts
| Medium Of Instructions | Mode Of Learning | Mode Of Delivery | Frequency Of Classes |
|---|---|---|---|
| English | Self Study, Virtual Classroom, Campus Based/Physical Classroom | Video and Text Based | Weekends |
Course Overview
As the world progresses ahead in all the fields, Artificial Intelligence also advances ahead even in the Healthcare department. More and more companies are shifting to it and adapting this way of saving lives and even statistics show better results with it. A person interested in the course has to have a certificate in this sector to grow.
That is where this course comes in. Advanced Certification Programme in Digital Health and Imaging teaches the candidates all about biotech devices and how to use data analytics to their best. Moreover, it teaches how the landscape of the healthcare system is changing with the introduction of the Deployment of Artificial Intelligence, the Internet of things along with other new coming technologies and delivery systems.
The contents of the course have been brought by the expert faculty of the Indian Institute of Sciences in association with TalentSprint. The course offers one-on-one mentoring with the absolute best faculty of India and with this course the candidates will learn so much about Medical Devices, Digital Therapeutics, and Personalized Healthcare that it will open up a great number of opportunities for them to grow and shine.
The Highlights
- 1:1 mentoring
- IISc certificate
- Unique design
- Expert faculty
- Online sessions
Programme Offerings
- Live classes.
Courses and Certificate Fees
| Certificate Availability | Certificate Providing Authority |
|---|---|
| yes | IISc Bangalore |
The fees for the course Advanced Certification Programme in Digital Health and Imaging is -
| Head | Amount in INR |
| Application Fee | ₹2,000 |
| Programme Fee | ₹2,50,000 |
| With Scholarship | ₹1,87,500 |
Eligibility Criteria
Work Experience
Candidates should have a minimum of one year of relevant experience in the industry to apply for the course.
Education
Candidates have to have a Bachelor’s degree or a Master’s degree in Science, Pharmacy, Engineering, Management, or Medicine.
What you will learn
Once the programme studying is completed, they will have a firm grasp on some of the most important concepts of the industry.
- They will learn all about Digital Health and how the candidates can use it to their best
- They will learn the fundamentals of machine learning and how to function it
- They will deep learn imaging and vision
- They will learn the know-how behind Physiological Signal Processing
- Finally, they will also learn about Wearable devices
Who it is for
The course is for professionals working in healthcare and technology, biotech, telemedicine, personalized healthcare, Digital therapeutics, and more among these lines.
Application Details
To get admission to the course, candidates have to follow the below-mentioned instructions for admission.
Step 1: Candidates have to visit the official course website.
Step 2: On the website, they will find a form which the candidates have to fill correctly and press on Apply Now.
Step 3: After the form has filled, they have to submit their documents.
Step 4: Once all the previous steps have been made, candidates have to wait to see if they get selected for the programme.
Step 5: If the candidates get selected, they will then be able to join the programme.
The Syllabus
Pre-requisites: Understanding of Digital Technology
- Need, case studies, basics - mHealth and eHealth, Impact
- Informatics: Health Level Seven (HL7), Integrating the Healthcare Enterprise (IHE), Vendor Neutral Archives (VNAs)
- Open source/data/innovation - opportunities
- IT infrastructure (IoT/Cloud computing)
Pre-requisites: Basics of Signals & Systems, Basics of Fourier Transforms and Z-Transforms, Basics of Physiology
- Signal Processing: Sampling, Basic Filters, Decimation, Interpolation, STFT, Wavelets
- Physiology: ECG Signal Acquisition (Electrical activity of heart, chest leads/montage, action potential in pacemaker and other regions; action potential relation to ECG Waveform; Reading ECG); EEG Signal Acquisition (Neural activity in the brain, Action potential, post-synaptic potential, Signal Propagation in the brain, EEG montage, EEG Signal Acquisition); EEG and ECG data processing
- Wearable Sensors for health monitoring: Accelerometers (data acquisition and interpretation), glucose sensing (acquisition methods and comparison), Wearable ECG & EEG based on dry electrodes
- Speech and audio signal processing: From signal capture to data pre-processing and feature modelling
Pre-requisites: Basic of Probability and Linear Algebra: Bayes Theorem, Random Variables, Expectation, Variance, Matrices, Inverse, Eigenvalues and Eigenvectors
- Basic Mathematics for ML, What is Data and Model? Machine Learning Workflow and Applications
- Introduction to real-world signals - text, speech, image, video; Feature extraction and front-end signal processing - information-rich representations, robustness to noise and artifacts
- Learning as optimization, Linear Regression, Regularization and Logistic Regression
- Basics of pattern recognition, Generative modelling - Gaussian and mixture Gaussian models
- Machine Learning for physiological signal processing. Time series modelling
Pre-requisites: Basic Machine Learning that is part of Module 3
- Deep Learning: Basics, MLPs, Back propagation, CNNs
- Deep Learning for physiological signal processing. Recurrent neural models
- Discussion on Depth Versus Width. Practical considerations in Deep Learning. Avoiding Overfitting- Regularization, Dropout. Convolutional Neural Networks. Recurrent Neural Networks. Forward and Backward propagation. Various Architectures for sequence to sequence and sequence to vector mapping.
- Applications of Deep, Convolutional and Recurrent models in healthcare. (Instructor: SG)
- Nature Language Processing: LSTMs, Language Models, Knowledge Graphs, Q&A (Demo)
Pre-requisites: Modules-1,2,3, and 4
- Medical Imaging Modalities: Introduction, Protocols, Work Flows, Applications
- Medical Image Analysis: Basics, Imaging Physics-Based Methods, and Need for Deep Learning & Neuroimaging: Introduction, Challenges
- Vision - Deep learning: Loss function, Optimization, CNNs, Training Convolutional Neural Networks, Object Detection, Segmentation
- Deep Learning models: AlexNet, VGG, GoogleNet, ResNet, RNN/LSTM
- Python Basics
- Basics of Probability and Statistics
- Basics of Linear Algebra
- Google Co-lab & examples
Instructors
IISc Bangalore Frequently Asked Questions (FAQ's)
Career Accelerator offered by TalentSprint in this course offers a compelling Profile, membership into an elite community, Priority Career Access, and startup mentorship.
Candidates after having finished this course can apply for the job of a research scientist, healthcare data analyst, senior medical engineering software engineer, and many more other fruitful career aspects.
Candidates have to have a bachelor’s degree of four years or equivalent or a Master’s degree in Science, Pharmacy, Engineering or Medicine. They also need one year of relevant work experience to be eligible for the course.