10 citations · 47 across the 20 of their papers we have counts for
9 papers · 1 filter
EMRs with Blockchain : A distributed democratised Electronic Medical Record sharing platform
Sanket Shevkar, Parthit Patel, Saptarshi Majumder +3
Medical data sharing needs to be done with the utmost respect for privacy and security. It contains intimate data of the patient and any access to it must be highly regulated. With…
Unsupervised deep clustering and reinforcement learning can accurately segment MRI brain tumors with very small training sets
Joseph Stember, Hrithwik Shalu
Purpose: Lesion segmentation in medical imaging is key to evaluating treatment response. We have recently shown that reinforcement learning can be applied to radiological images fo…
Multi-Modal Detection of Alzheimer's Disease from Speech and Text
Amish Mittal, Sourav Sahoo, Arnhav Datar +3
Reliable detection of the prodromal stages of Alzheimer's disease (AD) remains difficult even today because, unlike other neurocognitive impairments, there is no definitive diagnos…
Depression Status Estimation by Deep Learning based Hybrid Multi-Modal Fusion Model
Hrithwik Shalu, Harikrishnan P, Hari Sankar CN +6
Preliminary detection of mild depression could immensely help in effective treatment of the common mental health disorder. Due to the lack of proper awareness and the ample mix of…
A smartphone based multi input workflow for non-invasive estimation of haemoglobin levels using machine learning techniques
Sarah, S. Sidhartha Narayan, Irfaan Arif +2
We suggest a low cost, non invasive healthcare system that measures haemoglobin levels in patients and can be used as a preliminary diagnostic test for anaemia. A combination of im…
A Novel Sentiment Analysis Engine for Preliminary Depression Status Estimation on Social Media
Sudhir Kumar Suman, Hrithwik Shalu, Lakshya A Agrawal +2
Text sentiment analysis for preliminary depression status estimation of users on social media is a widely exercised and feasible method, However, the immense variety of users acces…
Reinforcement learning using Deep Q Networks and Q learning accurately localizes brain tumors on MRI with very small training sets
Joseph N Stember, Hrithwik Shalu
Purpose Supervised deep learning in radiology suffers from notorious inherent limitations: 1) It requires large, hand-annotated data sets, 2) It is non-generalizable, and 3) It lac…
A Data-Efficient Deep Learning Based Smartphone Application For Detection Of Pulmonary Diseases Using Chest X-rays
Hrithwik Shalu, Harikrishnan P, Akash Das +3
This paper introduces a paradigm of smartphone application based disease diagnostics that may completely revolutionise the way healthcare services are being provided. Although prim…
Deep reinforcement learning to detect brain lesions on MRI: a proof-of-concept application of reinforcement learning to medical images
Joseph Stember, Hrithwik Shalu
Purpose: AI in radiology is hindered chiefly by: 1) Requiring large annotated data sets. 2) Non-generalizability that limits deployment to new scanners / institutions. And 3) Inade…