7 papers
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…
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…
MambaX-Net: Dual-Input Mamba-Enhanced Cross-Attention Network for Longitudinal Prostate MRI Segmentation
Yovin Yahathugoda, Davide Prezzi, Piyalitt Ittichaiwong +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…