1 code implementation • 17 Apr 2024 • Zeyu Zhang, Xuyin Qi, Mingxi Chen, Guangxi Li, Ryan Pham, Ayub Qassim, Ella Berry, Zhibin Liao, Owen Siggs, Robert Mclaughlin, Jamie Craig, Minh-Son To
The oxygen saturation level in the blood (SaO2) is crucial for health, particularly in relation to sleep-related breathing disorders.
1 code implementation • 3 Apr 2024 • Townim Faisal Chowdhury, Kewen Liao, Vu Minh Hieu Phan, Minh-Son To, Yutong Xie, Kevin Hung, David Ross, Anton Van Den Hengel, Johan W. Verjans, Zhibin Liao
Deep Neural Networks (DNNs) are widely used for visual classification tasks, but their complex computation process and black-box nature hinder decision transparency and interpretability.
1 code implementation • 12 Mar 2024 • Vu Minh Hieu Phan, Yutong Xie, Yuankai Qi, Lingqiao Liu, Liyang Liu, BoWen Zhang, Zhibin Liao, Qi Wu, Minh-Son To, Johan W. Verjans
Medical vision language pre-training (VLP) has emerged as a frontier of research, enabling zero-shot pathological recognition by comparing the query image with the textual descriptions for each disease.
1 code implementation • 12 Nov 2023 • Zeyu Zhang, Xuyin Qi, BoWen Zhang, Biao Wu, Hien Le, Bora Jeong, Zhibin Liao, Yunxiang Liu, Johan Verjans, Minh-Son To, Richard Hartley
This manual process is highly time-consuming and expensive, limiting the number of patients who can receive timely radiotherapy.
1 code implementation • 30 Jul 2023 • Minh Hieu Phan, Zhibin Liao, Johan W. Verjans, Minh-Son To
Extensive experiments demonstrate that MaskGAN outperforms state-of-the-art synthesis methods on a challenging pediatric dataset, where MR and CT scans are heavily misaligned due to rapid growth in children.
1 code implementation • 21 Mar 2022 • Zhibin Liao, Kewen Liao, Haifeng Shen, Marouska F. van Boxel, Jasper Prijs, Ruurd L. Jaarsma, Job N. Doornberg, Anton Van Den Hengel, Johan W. Verjans
Convolutional neural networks (CNNs) have gained significant popularity in orthopedic imaging in recent years due to their ability to solve fracture classification problems.
no code implementations • 6 Aug 2020 • Yutong Xie, Jianpeng Zhang, Zhibin Liao, Chunhua Shen, Johan Verjans, Yong Xia
In this paper, we propose the pairwise relation-based semi-supervised (PRS^2) model for gland segmentation on histology images.
1 code implementation • 27 Mar 2020 • Jianpeng Zhang, Yutong Xie, Guansong Pang, Zhibin Liao, Johan Verjans, Wenxin Li, Zongji Sun, Jian He, Yi Li, Chunhua Shen, Yong Xia
In this paper, we formulate the task of differentiating viral pneumonia from non-viral pneumonia and healthy controls into an one-class classification-based anomaly detection problem, and thus propose the confidence-aware anomaly detection (CAAD) model, which consists of a shared feature extractor, an anomaly detection module, and a confidence prediction module.
no code implementations • 2 Nov 2019 • Zhibin Liao, Hany Girgis, Amir Abdi, Hooman Vaseli, Jorden Hetherington, Robert Rohling, Ken Gin, Teresa Tsang, Purang Abolmaesumi
Nevertheless, the observer variability in the expert's assessment can impact the quality quantification accuracy.
1 code implementation • 16 Oct 2018 • Zhibin Liao, Tom Drummond, Ian Reid, Gustavo Carneiro
Furthermore, the proposed measurements also allow us to show that it is possible to optimise the training process with a new dynamic sampling training approach that continuously and automatically change the mini-batch size and learning rate during the training process.
no code implementations • 18 Nov 2015 • Zhibin Liao, Gustavo Carneiro
In this paper, we introduce a new deep convolutional neural network (ConvNet) module that promotes competition among a set of multi-scale convolutional filters.
Ranked #17 on Image Classification on MNIST
no code implementations • 3 Aug 2015 • Zhibin Liao, Gustavo Carneiro
The combination of deep learning models and piecewise linear activation functions allows for the estimation of exponentially complex functions with the use of a large number of subnetworks specialized in the classification of similar input examples.
Ranked #23 on Image Classification on MNIST