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 • 2 Mar 2023 • Adrian Galdran, Johan Verjans, Gustavo Carneiro, Miguel A. González Ballester
Delivering meaningful uncertainty estimates is essential for a successful deployment of machine learning models in the clinical practice.
no code implementations • 25 Jan 2022 • Gerard Snaauw, Michele Sasdelli, Gabriel Maicas, Stephan Lau, Johan Verjans, Mark Jenkinson, Gustavo Carneiro
We propose guiding the training of a deep learning-based registration method with MI estimation between an image-pair in an end-to-end trainable network.
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 • 23 Oct 2018 • Gerard Snaauw, Dong Gong, Gabriel Maicas, Anton Van Den Hengel, Wiro J. Niessen, Johan Verjans, Gustavo Carneiro
In this paper, we propose a learning method to train diagnosis models, where our approach is designed to work with relatively small datasets.