29 citations · 58 across the 9 of their papers we have counts for
9 papers
Spatial Action Review: A Visual Analytics Dashboard for Auditing Language-to-Action Hand-offs in Electron Microscopy
Samia Mohinta, Albert Cardona
Multimodal large language models (MLLMs) are increasingly explored as interfaces for scientific image analysis, where a visual question-answering (VQA) response may be paired with…
Catena: A Comprehensive Software Suite for Large-Scale Connectomics
Samia Mohinta, Pedro Gómez-Gálvez, Shi Yan Lee +5
The gold standard datasets for mapping connectomes are electron microscopy volumes of densely labeled neural tissue at nanometer resolution. Yet reconstructing and proofreading neu…
Fairboard: a quantitative framework for equity assessment of healthcare models
James K. Ruffle, Samia Mohinta, Chris Foulon +5
Despite there now being more than 1,000 FDA-authorised AI medical devices, formal equity assessments -- whether model performance is uniform across patient subgroups -- are rare. H…
Bidirectional human-AI collaboration in brain tumour assessments improves both expert human and AI agent performance
James K Ruffle, Samia Mohinta, Guilherme Pombo +13
The benefits of artificial intelligence (AI) human partnerships-evaluating how AI agents enhance expert human performance-are increasingly studied. Though rarely evaluated in healt…
Predicting brain tumour enhancement from non-contrast MR imaging with artificial intelligence: a multi-cohort retrospective diagnostic accuracy study
James K Ruffle, Samia Mohinta, Guilherme Pombo +13
Brain tumour MRI typically requires both pre- and post-contrast imaging, but gadolinium is not always desirable (frequent follow-up, renal impairment, allergy, paediatric patients)…
VASARI-auto: equitable, efficient, and economical featurisation of glioma MRI
James K Ruffle, Samia Mohinta, Kelly Pegoretti Baruteau +5
The VASARI MRI feature set is a quantitative system designed to standardise glioma imaging descriptions. Though effective, deriving VASARI is time-consuming and seldom used in clin…
Computational limits to the legibility of the imaged human brain
James K Ruffle, Robert J Gray, Samia Mohinta +5
Our knowledge of the organisation of the human brain at the population-level is yet to translate into power to predict functional differences at the individual-level, limiting clin…
Brain tumour genetic network signatures of survival
James K Ruffle, Samia Mohinta, Guilherme Pombo +6
Tumour heterogeneity is increasingly recognized as a major obstacle to therapeutic success across neuro-oncology. Gliomas are characterised by distinct combinations of genetic and…
Brain tumour segmentation with incomplete imaging data
James K Ruffle, Samia Mohinta, Robert J Gray +2
The complex heterogeneity of brain tumours is increasingly recognized to demand data of magnitudes and richness only fully-inclusive, large-scale collections drawn from routine cli…