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Showing 1–5 of 5 results
Advanced filters: Author: Julian Gilbey Clear advanced filters

  • Diffusion models are reframed by developing a generative blood cell classifier that performs reliably in low-data regimes, adapts to domain shifts, detects anomalies with robustness and provides uncertainty estimates that surpass clinical expert benchmarks.

    • Simon Deltadahl
    • Julian Gilbey
    • Parashkev Nachev
    ResearchOpen Access
    Nature Machine Intelligence
    Volume: 7, P: 1791-1803

  • Shadbahr et al. highlight the importance of evaluating imputation quality when building classification models for incomplete data. They demonstrate how a model built on poorly imputed data can compromise the classifier, and develop a new method for assessing imputation quality based on how well the overall data distribution is preserved.

    • Tolou Shadbahr
    • Michael Roberts
    • Carola-Bibiane Schönlieb
    ResearchOpen Access
    Communications Medicine
    Volume: 3, P: 1-15

  • Many machine learning-based approaches have been developed for the prognosis and diagnosis of COVID-19 from medical images and this Analysis identifies over 2,200 relevant published papers and preprints in this area. After initial screening, 62 studies are analysed and the authors find they all have methodological flaws standing in the way of clinical utility. The authors have several recommendations to address these issues.

    • Michael Roberts
    • Derek Driggs
    • Carola-Bibiane Schönlieb
    ResearchOpen Access
    Nature Machine Intelligence
    Volume: 3, P: 199-217

  • With the explosion of machine learning models of increasing complexity for research applications, more attention is needed for the development of good quality codebases. Sören Dittmer, Michael Roberts and colleagues discuss how to embrace guiding principles from traditional software engineering, including the approach to incrementally grow software, and to use two types of feedback loop, testing correctness and efficacy.

    • Sören Dittmer
    • Michael Roberts
    • Carola-Bibiane Schönlieb
    Reviews
    Nature Machine Intelligence
    Volume: 5, P: 681-686