30 citations · 66 across the 9 of their papers we have counts for
10 papers
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…
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…