10 citations · 20 across the 13 of their papers we have counts for
19 papers
A Self-Supervised Image Registration Approach for Measuring Local Response Patterns in Metastatic Ovarian Cancer
Inês P. Machado, Anna Reithmeir, Fryderyk Kogl +13
High-grade serous ovarian carcinoma (HGSOC) is characterised by significant spatial and temporal heterogeneity, typically manifesting at an advanced metastatic stage. A major chall…
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…
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…
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…
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…
Bridging the gap between AI and Healthcare sides: towards developing clinically relevant AI-powered diagnosis systems
Changhee Han, Leonardo Rundo, Kohei Murao +2
Despite the success of Convolutional Neural Network-based Computer-Aided Diagnosis research, its clinical applications remain challenging. Accordingly, developing medical Artificia…
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…
Synthesizing Diverse Lung Nodules Wherever Massively: 3D Multi-Conditional GAN-based CT Image Augmentation for Object Detection
Changhee Han, Yoshiro Kitamura, Akira Kudo +6
Accurate Computer-Assisted Diagnosis, relying on large-scale annotated pathological images, can alleviate the risk of overlooking the diagnosis. Unfortunately, in medical imaging,…
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…
USE-Net: incorporating Squeeze-and-Excitation blocks into U-Net for prostate zonal segmentation of multi-institutional MRI datasets
Leonardo Rundo, Changhee Han, Yudai Nagano +12
Prostate cancer is the most common malignant tumors in men but prostate Magnetic Resonance Imaging (MRI) analysis remains challenging. Besides whole prostate gland segmentation, th…
CNN-based Prostate Zonal Segmentation on T2-weighted MR Images: A Cross-dataset Study
Leonardo Rundo, Changhee Han, Jin Zhang +10
Prostate cancer is the most common cancer among US men. However, prostate imaging is still challenging despite the advances in multi-parametric Magnetic Resonance Imaging (MRI), wh…
Infinite Brain MR Images: PGGAN-based Data Augmentation for Tumor Detection
Changhee Han, Leonardo Rundo, Ryosuke Araki +4
Due to the lack of available annotated medical images, accurate computer-assisted diagnosis requires intensive Data Augmentation (DA) techniques, such as geometric/intensity transf…
Learning More with Less: Conditional PGGAN-based Data Augmentation for Brain Metastases Detection Using Highly-Rough Annotation on MR Images
Changhee Han, Kohei Murao, Tomoyuki Noguchi +5
Accurate Computer-Assisted Diagnosis, associated with proper data wrangling, can alleviate the risk of overlooking the diagnosis in a clinical environment. Towards this, as a Data…
GenHap: A Novel Computational Method Based on Genetic Algorithms for Haplotype Assembly
Andrea Tangherloni, Simone Spolaor, Leonardo Rundo +6
The computational problem of inferring the full haplotype of a cell starting from read sequencing data is known as haplotype assembly, and consists in assigning all heterozygous Si…