no code implementations • 13 Jul 2022 • Ziyi Shen, Qianye Yang, Yuming Shen, Francesco Giganti, Vasilis Stavrinides, Richard Fan, Caroline Moore, Mirabela Rusu, Geoffrey Sonn, Philip Torr, Dean Barratt, Yipeng Hu
Image registration is useful for quantifying morphological changes in longitudinal MR images from prostate cancer patients.
1 code implementation • 20 Feb 2022 • Shaheer U. Saeed, Wen Yan, Yunguan Fu, Francesco Giganti, Qianye Yang, Zachary M. C. Baum, Mirabela Rusu, Richard E. Fan, Geoffrey A. Sonn, Mark Emberton, Dean C. Barratt, Yipeng Hu
This allows for the trained IQA controller to measure the impact an image has on the target task performance, when this task is performed using the predictor, e. g. segmentation and classification neural networks in modern clinical applications.
no code implementations • 19 Sep 2021 • Eleni Chiou, Francesco Giganti, Shonit Punwani, Iasonas Kokkinos, Eleftheria Panagiotaki
Firstly, we introduce a semantic cycle-consistency loss in the source domain to ensure that the translation preserves the semantics.
no code implementations • 16 Jan 2021 • Qianye Yang, Tom Vercauteren, Yunguan Fu, Francesco Giganti, Nooshin Ghavami, Vasilis Stavrinides, Caroline Moore, Matt Clarkson, Dean Barratt, Yipeng Hu
Organ morphology is a key indicator for prostate disease diagnosis and prognosis.
2 code implementations • 14 Oct 2020 • Eleni Chiou, Francesco Giganti, Shonit Punwani, Iasonas Kokkinos, Eleftheria Panagiotaki
Domain adaptation methods partially mitigate this problem by translating training data from a related source domain to a novel target domain, but typically assume that a one-to-one translation is possible.
no code implementations • 29 Aug 2020 • Qianye Yang, Yunguan Fu, Francesco Giganti, Nooshin Ghavami, Qingchao Chen, J. Alison Noble, Tom Vercauteren, Dean Barratt, Yipeng Hu
Morphological analysis of longitudinal MR images plays a key role in monitoring disease progression for prostate cancer patients, who are placed under an active surveillance program.