A5065644254- Geology and Paleoclimatology Research
- Machine Learning in Healthcare
- Radiomics and Machine Learning in Medical Imaging
- AI in cancer detection
- Geological formations and processes
- Digital Radiography and Breast Imaging
- Inorganic and Organometallic Chemistry
- Synthesis and Reactions of Organic Compounds
- Asymmetric Synthesis and Catalysis
- Synthesis and Reactivity of Heterocycles
- Radiology practices and education
- Chemical Synthesis and Analysis
- Molecular spectroscopy and chirality
- COVID-19 diagnosis using AI
- Synthetic Organic Chemistry Methods
- Biomedical Text Mining and Ontologies
- Topic Modeling
- Advanced X-ray and CT Imaging
- Structural and Chemical Analysis of Organic and Inorganic Compounds
- Coastal wetland ecosystem dynamics
- Chemical synthesis and alkaloids
- Landslides and related hazards
- Geological Modeling and Analysis
- Pleistocene-Era Hominins and Archaeology
- Isotope Analysis in Ecology
University of Cambridge
2021-2025
Cambridge University Hospitals NHS Foundation Trust
2023-2025
AstraZeneca (United Kingdom)
2021
Lung Institute
2021
Cambridge Hospital
2021
Queen Elizabeth Hospital
2019
Crown Estate (United Kingdom)
2012-2016
Great Yarmouth College
1998
Reckitt Benckiser (Netherlands)
1973
Removing the bias and variance of multicentre data has always been a challenge in large scale digital healthcare studies, which requires ability to integrate clinical features extracted from acquired by different scanners protocols improve stability robustness. Previous studies have described various computational approaches fuse single modality datasets. However, these surveys rarely focused on evaluation metrics lacked checklist for harmonisation studies. In this systematic review, we...
Abstract Background Classifying samples in incomplete datasets is a common aim for machine learning practitioners, but non-trivial. Missing data found most real-world and these missing values are typically imputed using established methods, followed by classification of the now complete samples. The focus researcher to optimise classifier’s performance. Methods We utilise three simulated clinical with different feature types missingness patterns. Initially, we evaluate how downstream...
Image quality assessment (IQA) is indispensable in clinical practice to ensure high standards, as well the development stage of machine learning algorithms that operate on medical images. The popular full reference (FR) IQA measures PSNR and SSIM are known tested for working successfully many natural imaging tasks, but discrepancies scenarios have been reported literature, highlighting gap between actual application. Such inconsistencies not surprising, images very different properties than...
"Just Accepted" papers have undergone full peer review and been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, proof before it is published its final version. Please note that during production of the copyedited article, errors may be discovered which could affect content. presents a suite quality control tools chest radiographs based on traditional artificial intelligence methods, developed tested with data from 39 centers 7...
Abstract Examination of two radiocarbon‐dated vibrocores taken from south St Kilda at a water depth about 155 m, short distance within the maximum position Late Devensian (Dimlington Stadial) ice sheet, suggests that Basin became free glacier after 15250 yr BP. Sedimentation in shallow, low energy, high arctic, muddy environment continued until 13500 There followed higher energy temperate episode during which depths were roughly 40 m: this is correlated with latter part Windermere...
The erosional morphology preserved at the sea bed in eastern English Channel dominantly records denudation of continental shelf by fluvial processes over multiple glacial-interglacial sea-level cycles rather than catastrophic flooding through Straits Dover during mid-Quaternary. Here, integration multibeam bathymetry and shallow sub-bottom 2D seismic reflection profiles calibrated with vibrocore records, first stratigraphic model erosion deposition on is presented. Published Optical...
Artificial intelligence (AI) systems for automated chest x-ray interpretation hold promise standardising reporting and reducing delays in health with shortages of trained radiologists. Yet, there are few freely accessible AI on large datasets practitioners to use their own data a view accelerating clinical deployment radiology. We aimed contribute an system comprehensive abnormality detection.
D. J. Kennedy, I. A. Selby, H. Cowe, P. Cox and R. Thomson, Chem. Soc., Commun., 1984, 153 DOI: 10.1039/C39840000153
J. W. Lewis, M. Readhead, I. A. Selby, C. B. Smith and Young, Chem. Soc. C, 1971, 1158 DOI: 10.1039/J39710001158
Dimethyl acetylenedicarboxylate and other acetylenic esters add to phenanthridine 5-oxides give phenanthridinium-5-vinyloxides, which, if a 6-alkyl group is present, readily cyclise methoxycarbonylpyrrolo[1,2-f]-phenanthridines. Hydrolysis decarboxylation pyrrolo[1,2-f]phenanthridine derivatives. The nuclear magnetic resonance spectra of these compounds have been partially analysed.
Abstract The National COVID-19 Chest Imaging Database (NCCID) is a centralized UK database of thoracic imaging and corresponding clinical data. It made available by the Health Service Artificial Intelligence (NHS AI) Lab to support development machine learning tools focused on Coronavirus Disease 2019 (COVID-19). A bespoke cleaning pipeline for NCCID, developed NHSx, was introduced in 2021. We present an extension original data database. has been adjusted correct additional systematic...
R. M. Acheson, A. S. Bailey and I. Selby, Chem. Commun. (London), 1966, 835a DOI: 10.1039/C1966000835A
The existence of enantiomeric rotational isomers in solutions certain 5-oxidovinylphenanthridiniums and other sterically hindered molecules has been demonstrated by 1H n.m.r. spectroscopy using tris-[3-(trifluoromethylhydroxymethylene)-(+)-camphorato]europium(III).
The Hofmann degradation of flavothebaone trimethyl ether ψ-methine has been re-examined and shown to give, as major product, 2-[3,6-dimethoxy-2-(2-oxopropyl)phenyl]-7,8-dimethoxy-1-vinylnaphthalene (6). A minor previously considered be the ketone (9), identified cyclic enol (11) derived from