20 citations · 43 across the 14 of their papers we have counts for
14 papers
RAMP: Robust Ad Recommendation Under Limited Personalized-Feature Availability via Masking and Alignment Pathways
Dairui Liu, Zhongyi Lu, Roger Zhe Li +11
Click-through rate (CTR) and conversion rate (CVR) prediction are fundamental tasks in online advertising, aiming to estimate the likelihood of user interactions based on various f…
DyABD: The Abdominal Muscle Segmentation in Dynamic MRI Benchmark
Niamh Belton, Victoria Joppin, Aonghus Lawlor +4
This work introduces DyABD, a novel and complex benchmark dataset of dynamic abdominal MRIs from patients with abdominal hernias and associated high quality abdominal muscle annota…
Is Complete Labeling Necessary? Understanding Active Learning in Longitudinal Medical Imaging
Siteng Ma, Honghui Du, Prateek Mathur +4
Detecting changes in longitudinal medical imaging using deep learning requires a substantial amount of accurately labeled data. However, labeling these images is notably more costl…
Multi-Label Transfer Learning in Non-Stationary Data Streams
Honghui Du, Leandro Minku, Aonghus Lawlor +1
Label concepts in multi-label data streams often experience drift in non-stationary environments, either independently or in relation to other labels. Transferring knowledge betwee…
Deep Learning Approaches for Medical Imaging Under Varying Degrees of Label Availability: A Comprehensive Survey
Siteng Ma, Honghui Du, Yu An +5
Deep learning has achieved significant breakthroughs in medical imaging, but these advancements are often dependent on large, well-annotated datasets. However, obtaining such datas…
Transformers4NewsRec: A Transformer-based News Recommendation Framework
Dairui Liu, Honghui Du, Boming Yang +5
Pre-trained transformer models have shown great promise in various natural language processing tasks, including personalized news recommendations. To harness the power of these mod…
An AI System for Continuous Knee Osteoarthritis Severity Grading Using Self-Supervised Anomaly Detection with Limited Data
Niamh Belton, Aonghus Lawlor, Kathleen M. Curran
The diagnostic accuracy and subjectivity of existing Knee Osteoarthritis (OA) ordinal grading systems has been a subject of on-going debate and concern. Existing automated solution…
Rethinking Knee Osteoarthritis Severity Grading: A Few Shot Self-Supervised Contrastive Learning Approach
Niamh Belton, Misgina Tsighe Hagos, Aonghus Lawlor +1
Knee Osteoarthritis (OA) is a debilitating disease affecting over 250 million people worldwide. Currently, radiologists grade the severity of OA on an ordinal scale from zero to fo…
Breaking the Barrier: Selective Uncertainty-based Active Learning for Medical Image Segmentation
Siteng Ma, Haochang Wu, Aonghus Lawlor +1
Active learning (AL) has found wide applications in medical image segmentation, aiming to alleviate the annotation workload and enhance performance. Conventional uncertainty-based…
RecPrompt: A Self-tuning Prompting Framework for News Recommendation Using Large Language Models
Dairui Liu, Boming Yang, Honghui Du +5
News recommendations heavily rely on Natural Language Processing (NLP) methods to analyze, understand, and categorize content, enabling personalized suggestions based on user inter…
Can We Transfer Noise Patterns? A Multi-environment Spectrum Analysis Model Using Generated Cases
Haiwen Du, Zheng Ju, Yu An +5
Spectrum analysis systems in online water quality testing are designed to detect types and concentrations of pollutants and enable regulatory agencies to respond promptly to pollut…
FewSOME: One-Class Few Shot Anomaly Detection with Siamese Networks
Niamh Belton, Misgina Tsighe Hagos, Aonghus Lawlor +1
Recent Anomaly Detection techniques have progressed the field considerably but at the cost of increasingly complex training pipelines. Such techniques require large amounts of trai…
Optimising Knee Injury Detection with Spatial Attention and Validating Localisation Ability
Niamh Belton, Ivan Welaratne, Adil Dahlan +4
This work employs a pre-trained, multi-view Convolutional Neural Network (CNN) with a spatial attention block to optimise knee injury detection. An open-source Magnetic Resonance I…
Semi-Supervised Siamese Network for Identifying Bad Data in Medical Imaging Datasets
Niamh Belton, Aonghus Lawlor, Kathleen M. Curran
Noisy data present in medical imaging datasets can often aid the development of robust models that are equipped to handle real-world data. However, if the bad data contains insuffi…