activity
20202026
most citedCommon pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans

1k citations · 1.1k across the 6 of their papers we have counts for

collaborators

6 papers

cs.CV2026★ 1 cited

Developing Predictive and Robust Radiomics Models for Chemotherapy Response in High-Grade Serous Ovarian Carcinoma

Sepideh Hatamikia, Geevarghese George, Florian Schwarzhans +10

Objectives: High-grade serous ovarian carcinoma (HGSOC) is typically diagnosed at an advanced stage with extensive peritoneal metastases, making treatment challenging. Neoadjuvant…

physics.med-ph2024★ 3 cited

Intensity Normalization Techniques and Their Effect on the Robustness and Predictive Power of Breast MRI Radiomics

Florian Schwarzhans, Geevarghese George, Lorena Escudero Sanchez +4

Radiomics analysis has emerged as a promising approach for extracting quantitative features from medical images to aid in cancer diagnosis and treatment. However, radiomics researc…

cs.SE2022★ 11 cited

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…

cs.LG2022

Calibrating Ensembles for Scalable Uncertainty Quantification in Deep Learning-based Medical Segmentation

Thomas Buddenkotte, Lorena Escudero Sanchez, Mireia Crispin-Ortuzar +6

Uncertainty quantification in automated image analysis is highly desired in many applications. Typically, machine learning models in classification or segmentation are only develop…

cs.AI2021★ 78 cited

Advancing COVID-19 Diagnosis with Privacy-Preserving Collaboration in Artificial Intelligence

Xiang Bai, Hanchen Wang, Liya Ma +43

Artificial intelligence (AI) provides a promising substitution for streamlining COVID-19 diagnoses. However, concerns surrounding security and trustworthiness impede the collection…

cs.LG2020★ 1k cited

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 (…