6 citations · 8 across the 5 of their papers we have counts for
5 papers
Uni-Light: An Ultra-Lightweight Framework via Uncertainty-Aware Knowledge Distillation for Brain Tumour Segmentation
Libing Kuang, Soren Salehi, Ziling Wu +2
Accurate 3D brain tumour segmentation from multi-modal Magnetic Resonance Imaging (MRI) is essential for clinical diagnosis and treatment planning. Existing brain tumour segmentati…
Ensemble Foreground Management for Unsupervised Object Discovery
Ziling Wu, Armaghan Moemeni, Praminda Caleb-Solly
Unsupervised object discovery (UOD) aims to detect and segment objects in 2D images without handcrafted annotations. Recent progress in self-supervised representation learning has…
Dealing with Distribution Mismatch in Semi-supervised Deep Learning for Covid-19 Detection Using Chest X-ray Images: A Novel Approach Using Feature Densities
Saul Calderon-Ramirez, Shengxiang Yang, David Elizondo +1
In the context of the global coronavirus pandemic, different deep learning solutions for infected subject detection using chest X-ray images have been proposed. However, deep learn…
Correcting Data Imbalance for Semi-Supervised Covid-19 Detection Using X-ray Chest Images
Saul Calderon-Ramirez, Shengxiang-Yang, Armaghan Moemeni +4
The Corona Virus (COVID-19) is an internationalpandemic that has quickly propagated throughout the world. The application of deep learning for image classification of chest X-ray i…
MixMOOD: A systematic approach to class distribution mismatch in semi-supervised learning using deep dataset dissimilarity measures
Saul Calderon-Ramirez, Luis Oala, Jordina Torrents-Barrena +4
In this work, we propose MixMOOD - a systematic approach to mitigate effect of class distribution mismatch in semi-supervised deep learning (SSDL) with MixMatch. This work is divid…