10 citations · 20 across the 13 of their papers we have counts for
9 papers · 1 filter
Calibrating the Dice loss to handle neural network overconfidence for biomedical image segmentation
Michael Yeung, Leonardo Rundo, Yang Nan +3
The Dice similarity coefficient (DSC) is both a widely used metric and loss function for biomedical image segmentation due to its robustness to class imbalance. However, it is well…
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…
Unified Focal loss: Generalising Dice and cross entropy-based losses to handle class imbalanced medical image segmentation
Michael Yeung, Evis Sala, Carola-Bibiane Schönlieb +1
Automatic segmentation methods are an important advancement in medical image analysis. Machine learning techniques, and deep neural networks in particular, are the state-of-the-art…
3D deformable registration of longitudinal abdominopelvic CT images using unsupervised deep learning
Maureen van Eijnatten, Leonardo Rundo, K. Joost Batenburg +7
This study investigates the use of the unsupervised deep learning framework VoxelMorph for deformable registration of longitudinal abdominopelvic CT images acquired in patients wit…
GAN-based Multiple Adjacent Brain MRI Slice Reconstruction for Unsupervised Alzheimer's Disease Diagnosis
Changhee Han, Leonardo Rundo, Kohei Murao +5
Unsupervised learning can discover various unseen diseases, relying on large-scale unannotated medical images of healthy subjects. Towards this, unsupervised methods reconstruct a…
Combining Noise-to-Image and Image-to-Image GANs: Brain MR Image Augmentation for Tumor Detection
Changhee Han, Leonardo Rundo, Ryosuke Araki +5
Convolutional Neural Networks (CNNs) achieve excellent computer-assisted diagnosis with sufficient annotated training data. However, most medical imaging datasets are small and fra…