2.7k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- University of CambridgeGB13 papers
- University College LondonGB7 papers
- Imperial College LondonGB5 papers
- Medical University of ViennaAT5 papers
- Addenbrooke's HospitalGB4 papers
- AstraZeneca (United Kingdom)GB3 papers
- Cancer Research UK Cambridge CenterGB3 papers
- Genomics (United Kingdom)GB3 papers
- Harvard UniversityUS3 papers
- King's College LondonGB3 papers
- University of DundeeGB3 papers
- University of HelsinkiFI3 papers
18 papers
Causal Machine Learning Is Not a Panacea: A Roadmap for Observational Causal Inference in Health
Donna Tjandra, Trenton Chang, Sonali Parbhoo +8
Objective: The growing availability of large-scale observational clinical datasets and challenges in conducting randomized controlled trials have spurred enthusiasm in using causal…
Combining multiplexed functional data to improve variant classification
Atlas of Variant Effects Alliance, :, Jeffrey D. Calhoun +10
With the surge in the number of variants of uncertain significance (VUS) reported in ClinVar in recent years, there is an imperative to resolve VUS at scale. Multiplexed assays of…
KMT2B-related disorders: expansion of the phenotypic spectrum and long-term efficacy of deep brain stimulation
L Cif, D Demailly, JP Lin +112
Heterozygous mutations in KMT2B are associated with an early-onset, progressive, and often complex dystonia (DYT28). Key characteristics of typical disease include focal motor feat…
PadChest-GR: A Bilingual Chest X-ray Dataset for Grounded Radiology Report Generation
Daniel C. Castro, Aurelia Bustos, Shruthi Bannur +11
Radiology report generation (RRG) aims to create free-text radiology reports from clinical imaging. Grounded radiology report generation (GRRG) extends RRG by including the localis…
Parameter choices in HaarPSI for IQA with medical images
Clemens Karner, Janek Gröhl, Ian Selby +11
When developing machine learning models, image quality assessment (IQA) measures are a crucial component for the evaluation of obtained output images. However, commonly used full-r…
A study on the adequacy of common IQA measures for medical images
Anna Breger, Clemens Karner, Ian Selby +11
Image quality assessment (IQA) is standard practice in the development stage of novel machine learning algorithms that operate on images. The most commonly used IQA measures have b…
Multimodal Healthcare AI: Identifying and Designing Clinically Relevant Vision-Language Applications for Radiology
Nur Yildirim, Hannah Richardson, Maria T. Wetscherek +18
Recent advances in AI combine large language models (LLMs) with vision encoders that bring forward unprecedented technical capabilities to leverage for a wide range of healthcare a…
The NOSTRA model: coherent estimation of infection sources in the case of possible nosocomial transmission
David J Pascall, Chris Jackson, Stephanie Evans +8
Nosocomial infections have important consequences for patients and hospital staff: they worsen patient outcomes and their management stresses already overburdened health systems. A…
Exploring scalable medical image encoders beyond text supervision
Fernando Pérez-García, Harshita Sharma, Sam Bond-Taylor +12
Language-supervised pre-training has proven to be a valuable method for extracting semantically meaningful features from images, serving as a foundational element in multimodal sys…
Going faster to see further: GPU-accelerated value iteration and simulation for perishable inventory control using JAX
Joseph Farrington, Kezhi Li, Wai Keong Wong +1
Value iteration can find the optimal replenishment policy for a perishable inventory problem, but is computationally demanding due to the large state spaces that are required to re…
Sample size determination via learning-type curves
Alimu Dayimu, Nikola Simidjievski, Nikolaos Demiris +1
This paper is concerned with sample size determination methodology for prediction models. We propose combining the individual calculations via a learning-type curve. We suggest two…
Navigating the challenges in creating complex data systems: a development philosophy
Sören Dittmer, Michael Roberts, Julian Gilbey +6
In this perspective, we argue that despite the democratization of powerful tools for data science and machine learning over the last decade, developing the code for a trustworthy a…
Classification of datasets with imputed missing values: does imputation quality matter?
Tolou Shadbahr, Michael Roberts, Jan Stanczuk +15
Classifying samples in incomplete datasets is a common aim for machine learning practitioners, but is non-trivial. Missing data is found in most real-world datasets and these missi…
Transductive image segmentation: Self-training and effect of uncertainty estimation
Konstantinos Kamnitsas, Stefan Winzeck, Evgenios N. Kornaropoulos +9
Semi-supervised learning (SSL) uses unlabeled data during training to learn better models. Previous studies on SSL for medical image segmentation focused mostly on improving model…
Machine Learning and Glioblastoma: Treatment Response Monitoring Biomarkers in 2021
Thomas Booth, Bernice Akpinar, Andrei Roman +10
The aim of the systematic review was to assess recently published studies on diagnostic test accuracy of glioblastoma treatment response monitoring biomarkers in adults, developed…
Population Properties of Compact Objects from the Second LIGO-Virgo Gravitational-Wave Transient Catalog
The LIGO Scientific Collaboration, the Virgo Collaboration, R. Abbott +1338
We report on the population of the 47 compact binary mergers detected with a false-alarm rate 1/yr in the second LIGO--Virgo Gravitational-Wave Transient Catalog, GWTC-2. We observ…
Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans
Michael Roberts, Derek Driggs, Matthew Thorpe +13
Machine learning methods offer great promise for fast and accurate detection and prognostication of COVID-19 from standard-of-care chest radiographs (CXR) and computed tomography (…
SARS-CoV-2 Entry Genes Are Most Highly Expressed in Nasal Goblet and Ciliated Cells within Human Airways
Waradon Sungnak, Ni Huang, Christophe Bécavin +2
The SARS-CoV-2 coronavirus, the etiologic agent responsible for COVID-19 coronavirus disease, is a global threat. To better understand viral tropism, we assessed the RNA expression…