3 papers
eess.IV2026
Towards Segmenting the Invisible: An End-to-End Registration and Segmentation Framework for Weakly Supervised Tumour Analysis
Budhaditya Mukhopadhyay, Chirag Mandal, Pavan Tummala +3
Liver tumour ablation presents a significant clinical challenge: whilst tumours are clearly visible on pre-operative MRI, they are often effectively invisible on intra-operative CT…
eess.IV2026
Weakly-supervised segmentation using inherently-explainable classification models and their application to brain tumour classification
Soumick Chatterjee, Hadya Yassin, Florian Dubost +2
Deep learning has demonstrated significant potential in medical imaging; however, the opacity of "black-box" models hinders clinical trust, while segmentation tasks typically neces…
cs.CV2025
PULASki: Learning inter-rater variability using statistical distances to improve probabilistic segmentation
Soumick Chatterjee, Franziska Gaidzik, Alessandro Sciarra +5
In the domain of medical imaging, many supervised learning based methods for segmentation face several challenges such as high variability in annotations from multiple experts, pau…