39 citations · 39 across the 2 of their papers we have counts for
5 papers
Deep Ensemble approach for Enhancing Brain Tumor Segmentation in Resource-Limited Settings
Jeremiah Fadugba, Isabel Lieberman, Olabode Ajayi +6
Segmentation of brain tumors is a critical step in treatment planning, yet manual segmentation is both time-consuming and subjective, relying heavily on the expertise of radiologis…
Bridging the Gap: Generalising State-of-the-Art U-Net Models to Sub-Saharan African Populations
Alyssa R. Amod, Alexandra Smith, Pearly Joubert +6
A critical challenge for tumour segmentation models is the ability to adapt to diverse clinical settings, particularly when applied to poor-quality neuroimaging data. The uncertain…
FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare
Karim Lekadir, Aasa Feragen, Abdul Joseph Fofanah +117
Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. I…
Dynamic multi feature-class Gaussian process models
Jean-Rassaire Fouefack, Bhushan Borotikar, Marcel Lüthi +3
In model-based medical image analysis, three features of interest are the shape of structures of interest, their relative pose, and image intensity profiles representative of some…
Dynamic multi-object Gaussian process models: A framework for data-driven functional modelling of human joints
Jean-Rassaire Fouefack, Bhushan Borotikar, Tania S. Douglas +2
Statistical shape models (SSMs) are state-of-the-art medical image analysis tools for extracting and explaining features across a set of biological structures. However, a principle…