165 citations · 240 across the 13 of their papers we have counts for
13 papers
Deep Offline Reinforcement Learning for Real-world Treatment Optimization Applications
Milashini Nambiar, Supriyo Ghosh, Priscilla Ong +3
There is increasing interest in data-driven approaches for recommending optimal treatment strategies in many chronic disease management and critical care applications. Reinforcemen…
Towards More Efficient Data Valuation in Healthcare Federated Learning using Ensembling
Sourav Kumar, A. Lakshminarayanan, Ken Chang +5
Federated Learning (FL) wherein multiple institutions collaboratively train a machine learning model without sharing data is becoming popular. Participating institutions might not…
Consistency-Based Semi-supervised Evidential Active Learning for Diagnostic Radiograph Classification
Shafa Balaram, Cuong M. Nguyen, Ashraf Kassim +1
Deep learning approaches achieve state-of-the-art performance for classifying radiology images, but rely on large labelled datasets that require resource-intensive annotation by sp…
Domain-specific Language Pre-training for Dialogue Comprehension on Clinical Inquiry-Answering Conversations
Zhengyuan Liu, Pavitra Krishnaswamy, Nancy F. Chen
There is growing interest in the automated extraction of relevant information from clinical dialogues. However, it is difficult to collect and construct large annotated resources f…
Semi-supervised classification of radiology images with NoTeacher: A Teacher that is not Mean
Balagopal Unnikrishnan, Cuong Nguyen, Shafa Balaram +3
Deep learning models achieve strong performance for radiology image classification, but their practical application is bottlenecked by the need for large labeled training datasets.…
Self-Path: Self-supervision for Classification of Pathology Images with Limited Annotations
Navid Alemi Koohbanani, Balagopal Unnikrishnan, Syed Ali Khurram +2
While high-resolution pathology images lend themselves well to `data hungry' deep learning algorithms, obtaining exhaustive annotations on these images is a major challenge. In thi…
Bayesian Recurrent Framework for Missing Data Imputation and Prediction with Clinical Time Series
Yang Guo, Zhengyuan Liu, Pavitra Krishnswamy +1
Real-world clinical time series data sets exhibit a high prevalence of missing values. Hence, there is an increasing interest in missing data imputation. Traditional statistical ap…
Joint Learning of Word and Label Embeddings for Sequence Labelling in Spoken Language Understanding
Jiewen Wu, Luis Fernando D'Haro, Nancy F. Chen +2
We propose an architecture to jointly learn word and label embeddings for slot filling in spoken language understanding. The proposed approach encodes labels using a combination of…
Fast Prototyping a Dialogue Comprehension System for Nurse-Patient Conversations on Symptom Monitoring
Zhengyuan Liu, Hazel Lim, Nur Farah Ain Binte Suhaimi +9
Data for human-human spoken dialogues for research and development are currently very limited in quantity, variety, and sources; such data are even scarcer in healthcare. In this w…
CaRENets: Compact and Resource-Efficient CNN for Homomorphic Inference on Encrypted Medical Images
Jin Chao, Ahmad Al Badawi, Balagopal Unnikrishnan +9
Convolutional neural networks (CNNs) have enabled significant performance leaps in medical image classification tasks. However, translating neural network models for clinical appli…
Semi-Supervised Deep Learning for Abnormality Classification in Retinal Images
Bruno Lecouat, Ken Chang, Chuan-Sheng Foo +7
Supervised deep learning algorithms have enabled significant performance gains in medical image classification tasks. But these methods rely on large labeled datasets that require…
Online Deep Learning: Growing RBM on the fly
Savitha Ramasamy, Kanagasabai Rajaraman, Pavitra Krishnaswamy +1
We propose a novel online learning algorithm for Restricted Boltzmann Machines (RBM), namely, the Online Generative Discriminative Restricted Boltzmann Machine (OGD-RBM), that prov…
Sparsity Enables Estimation of both Subcortical and Cortical Activity from MEG and EEG
Pavitra Krishnaswamy, Gabriel Obregon-Henao, Jyrki Ahveninen +5
Subcortical structures play a critical role in brain function. However, options for assessing electrophysiological activity in these structures are limited. Electromagnetic fields…