1.3k citations · 2.8k across the 16 of their papers we have counts for
18 papers
MID: Mask-Image Distributional Divergence for Evaluating Medical Image Segmentation
Vincenzo Marcianò, Xiaoming Zhang, Gianluca Guglielmo +3
Current evaluation protocols for medical image segmentation remain largely anchored in sample-wise comparisons, e.g., Dice and Hausdorff distance, which require each prediction to…
Making Cross-Continental Federated Learning Repeatable with FLIP: a Multi-Application Study
Rafael Garcia-Dias, Alexandre Triay Bagur, Chayanin Tangwiriyasakul +20
Federated learning (FL) in healthcare remains challenging, as the overhead of rebuilding governance guarantees for every collaboration stops most projects at the proof-of-concept s…
Right Regions, Wrong Labels: Semantic Label Flips in Segmentation under Correlation Shift
Akshit Achara, Yovin Yahathugoda, Nick Byrne +4
The robustness of machine learning models can be compromised by spurious correlations between non-causal features in the input data and target labels. A common way to test for such…
Performance uncertainty in medical image analysis: a large-scale investigation of confidence intervals
Pascaline André, Charles Heitz, Evangelia Christodoulou +10
Performance uncertainty quantification is essential for reliable validation and eventual clinical translation of medical imaging artificial intelligence (AI). Confidence intervals…
Diffusion-Based Quality Control of Medical Image Segmentations across Organs
Vincenzo Marcianò, Hava Chaptoukaev, Virginia Fernandez +4
Medical image segmentation using deep learning (DL) has enabled the development of automated analysis pipelines for large-scale population studies. However, state-of-the-art DL met…
MambaX-Net: Dual-Input Mamba-Enhanced Cross-Attention Network for Longitudinal Prostate MRI Segmentation
Yovin Yahathugoda, Davide Prezzi, Patricia A. Gutierrez +4
Active Surveillance (AS) is a treatment option for managing low and intermediate-risk prostate cancer (PCa), aiming to avoid overtreatment while monitoring disease progression thro…
False Promises in Medical Imaging AI? Assessing Validity of Outperformance Claims
Evangelia Christodoulou, Annika Reinke, Pascaline Andrè +23
Performance comparisons are fundamental in medical imaging Artificial Intelligence (AI) research, often driving claims of superiority based on relative improvements in common perfo…
Resolution Invariant Autoencoder
Ashay Patel, Michela Antonelli, Sebastien Ourselin +1
Deep learning has significantly advanced medical imaging analysis, yet variations in image resolution remain an overlooked challenge. Most methods address this by resampling images…
A cautionary tale on the cost-effectiveness of collaborative AI in real-world medical applications
Francesco Cremonesi, Lucia Innocenti, Sebastien Ourselin +3
Federated learning (FL) has gained wide popularity as a collaborative learning paradigm allowing collaborative Artificial Intelligence (AI) in sensitive healthcare applications. Ne…
Confidence intervals uncovered: Are we ready for real-world medical imaging AI?
Evangelia Christodoulou, Annika Reinke, Rola Houhou +19
Medical imaging is spearheading the AI transformation of healthcare. Performance reporting is key to determine which methods should be translated into clinical practice. Frequently…
Benchmarking Collaborative Learning Methods Cost-Effectiveness for Prostate Segmentation
Lucia Innocenti, Michela Antonelli, Francesco Cremonesi +5
Healthcare data is often split into medium/small-sized collections across multiple hospitals and access to it is encumbered by privacy regulations. This brings difficulties to use…
DeepEdit: Deep Editable Learning for Interactive Segmentation of 3D Medical Images
Andres Diaz-Pinto, Pritesh Mehta, Sachidanand Alle +17
Automatic segmentation of medical images is a key step for diagnostic and interventional tasks. However, achieving this requires large amounts of annotated volumes, which can be te…
Understanding metric-related pitfalls in image analysis validation
Annika Reinke, Minu D. Tizabi, Michael Baumgartner +75
Validation metrics are key for the reliable tracking of scientific progress and for bridging the current chasm between artificial intelligence (AI) research and its translation int…
Metrics reloaded: Recommendations for image analysis validation
Lena Maier-Hein, Annika Reinke, Patrick Godau +71
Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. Particularly in automatic biomedical image analysis, chosen…
The Medical Segmentation Decathlon
Michela Antonelli, Annika Reinke, Spyridon Bakas +55
International challenges have become the de facto standard for comparative assessment of image analysis algorithms given a specific task. Segmentation is so far the most widely inv…
Common Limitations of Image Processing Metrics: A Picture Story
Annika Reinke, Minu D. Tizabi, Carole H. Sudre +90
While the importance of automatic image analysis is continuously increasing, recent meta-research revealed major flaws with respect to algorithm validation. Performance metrics are…
Accessible Data Curation and Analytics for International-Scale Citizen Science Datasets
Benjamin Murray, Eric Kerfoot, Mark S. Graham +14
The Covid Symptom Study, a smartphone-based surveillance study on COVID-19 symptoms in the population, is an exemplar of big data citizen science. Over 4.7 million participants and…
A large annotated medical image dataset for the development and evaluation of segmentation algorithms
Amber L. Simpson, Michela Antonelli, Spyridon Bakas +21
Semantic segmentation of medical images aims to associate a pixel with a label in a medical image without human initialization. The success of semantic segmentation algorithms is c…