5 citations · 8 across the 10 of their papers we have counts for
16 papers
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…
Focal Attention Networks: optimising attention for biomedical image segmentation
Michael Yeung, Leonardo Rundo, Evis Sala +2
In recent years, there has been increasing interest to incorporate attention into deep learning architectures for biomedical image segmentation. The modular design of attention mec…
Incorporating Boundary Uncertainty into loss functions for biomedical image segmentation
Michael Yeung, Guang Yang, Evis Sala +2
Manual segmentation is used as the gold-standard for evaluating neural networks on automated image segmentation tasks. Due to considerable heterogeneity in shapes, colours and text…
Focus U-Net: A novel dual attention-gated CNN for polyp segmentation during colonoscopy
Michael Yeung, Evis Sala, Carola-Bibiane Schönlieb +1
Background: Colonoscopy remains the gold-standard screening for colorectal cancer. However, significant miss rates for polyps have been reported, particularly when there are multip…
Computer-Assisted Analysis of Biomedical Images
Leonardo Rundo
Nowadays, the amount of heterogeneous biomedical data is increasing more and more thanks to novel sensing techniques and high-throughput technologies. In reference to biomedical im…
MADGAN: unsupervised Medical Anomaly Detection GAN using multiple adjacent brain MRI slice reconstruction
Changhee Han, Leonardo Rundo, Kohei Murao +7
Unsupervised learning can discover various unseen abnormalities, relying on large-scale unannotated medical images of healthy subjects. Towards this, unsupervised methods reconstru…