Browsing by Keyword "info:eu-repo/grantAgreement/EC/H2020/952172/EU/Accelerating the lab to market transition of AI tools for cancer Management/CHAIMELEON"
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Item Data harmonisation for information fusion in digital healthcare: A state-of-the-art systematic review, meta-analysis and future research directions: A state-of-the-art systematic review, meta-analysis and future research directions(2022-06) Nan, Yang; Ser, Javier Del; Walsh, Simon; Schönlieb, Carola; Roberts, Michael; Selby, Ian; Howard, Kit; Owen, John; Neville, Jon; Guiot, Julien; Ernst, Benoit; Pastor, Ana; Alberich-Bayarri, Angel; Menzel, Marion I.; Walsh, Sean; Vos, Wim; Flerin, Nina; Charbonnier, Jean-Paul; van Rikxoort, Eva; Chatterjee, Avishek; Woodruff, Henry; Lambin, Philippe; Cerdá-Alberich, Leonor; Martí-Bonmatí, Luis; Herrera, Francisco; Yang, Guang; IARemoving the bias and variance of multicentre data has always been a challenge in large scale digital healthcare studies, which requires the ability to integrate clinical features extracted from data acquired by different scanners and protocols to improve stability and robustness. Previous studies have described various computational approaches to fuse single modality multicentre datasets. However, these surveys rarely focused on evaluation metrics and lacked a checklist for computational data harmonisation studies. In this systematic review, we summarise the computational data harmonisation approaches for multi-modality data in the digital healthcare field, including harmonisation strategies and evaluation metrics based on different theories. In addition, a comprehensive checklist that summarises common practices for data harmonisation studies is proposed to guide researchers to report their research findings more effectively. Last but not least, flowcharts presenting possible ways for methodology and metric selection are proposed and the limitations of different methods have been surveyed for future research.Item Swin transformer for fast MRI(2022-07-07) Huang, Jiahao; Fang, Yingying; Wu, Yinzhe; Wu, Huanjun; Gao, Zhifan; Li, Yang; Ser, Javier Del; Xia, Jun; Yang, Guang; IAMagnetic resonance imaging (MRI) is an important non-invasive clinical tool that can produce high-resolution and reproducible images. However, a long scanning time is required for high-quality MR images, which leads to exhaustion and discomfort of patients, inducing more artefacts due to voluntary movements of the patients and involuntary physiological movements. To accelerate the scanning process, methods by k-space undersampling and deep learning based reconstruction have been popularised. This work introduced SwinMR, a novel Swin transformer based method for fast MRI reconstruction. The whole network consisted of an input module (IM), a feature extraction module (FEM) and an output module (OM). The IM and OM were 2D convolutional layers and the FEM was composed of a cascaded of residual Swin transformer blocks (RSTBs) and 2D convolutional layers. The RSTB consisted of a series of Swin transformer layers (STLs). The shifted windows multi-head self-attention (W-MSA/SW-MSA) of STL was performed in shifted windows rather than the multi-head self-attention (MSA) of the original transformer in the whole image space. A novel multi-channel loss was proposed by using the sensitivity maps, which was proved to reserve more textures and details. We performed a series of comparative studies and ablation studies in the Calgary-Campinas public brain MR dataset and conducted a downstream segmentation experiment in the Multi-modal Brain Tumour Segmentation Challenge 2017 dataset. The results demonstrate our SwinMR achieved high-quality reconstruction compared with other benchmark methods, and it shows great robustness with different undersampling masks, under noise interruption and on different datasets. The code is publicly available at https://github.com/ayanglab/SwinMR.