Affiliated Research Centers

The following departments across the IIITH community are supporting this initiative and bridging the gap between technology and healthcare.

Focus Areas

Healthcare at IHub-Data aspires to foster a culture of AI research where technology advancements are intertwined with the societal impacts at population scale health and well-being. Here are the highlights on the multidisciplinary focus areas that help people to respond thoughtfully and pursue innovative research, create societally useful problem statements, and develop responsible AI solutions in healthcare.

Healthcare and Artificial Intelligence (HAI) internship Program

During the academic year 2020-21, Prof. C V Jawahar, Research Dean, IIITH, Prof. Deva Priyakumar U, Academic Head, iHub-Data and Prof. Bapi Raju S, Healthcare Head, iHub-Data and Dr. Vinod P.K, Assistant Professor played a key role in brewing up this major innovative interdisciplinary internship program that mainly focuses on leveraging artificial intelligence to solve some real-world challenges in the general area of healthcare.

HAI recruits' full-time interns across the country every year especially in the month of February and March. HAI provides opportunities to research fellows to work on our projects and get mentored by IIITH academia and industry. Every year a set of bright students are formally selected for the Masters by Research program at IIITH, as proof of their excellence and efficiency.

HAI 1.0 B (2021-2022)

Assessment of neurological disorder with audio-visuals

Multi signal analysis for emotion identification

Automatic sleep stage classification and prediction of stroke recurrence

Classification of AUtism Subtype and it's Exposition

Signal analysis on a low-resource wearable ECG device

Electrocardiography Question Answering

Deciphering XRay Models

AI for predicting pathogen mutation

Detecting Colorectal Cancer using Deep learning

HAI 1.0 A (2020-2021)

DeepPocket: Ligand Binding Site Detection and Segmentation using 3D Convolutional Neural Networks

LigGPT: Molecular Generation using a Transformer-Decoder Model

MMBERT: Multimodal BERT Pretraining for Improved Medical VQA

IMLE-Net: An Interpretable Multi-level Multi-channel Model for ECG Classification

Weakly supervised method for the classification of Astrocytoma and Oligodendroglioma using Histopathology Images

Deep learning in Nephropathology

Nephro-pathology is a discipline focusing on the diagnostic process of kidney diseases (Lupus nephritis, Diabetic nephropathy, Interstitial nephritis, etc.). This project focus on developing tools that help in the identification and characterization of glomeruli in health and disease. This work will involve segmentation and classification of glomeruli.

Characterization (Prediction) of Healthy Ageing using Deep Learning and rs-fMRI

Viral Host Prediction using Machine Learning

Mutation Prediction of Viruses using genomic/ proteomics

Deep Learning Approach for classification and Interpretation of Autism Spectrum Disorder