46 citations · 62 across the 4 of their papers we have counts for
5 papers · 1 filter
Many-shot from Low-shot: Learning to Annotate using Mixed Supervision for Object Detection
Carlo Biffi, Steven McDonagh, Philip Torr +2
Object detection has witnessed significant progress by relying on large, manually annotated datasets. Annotating such datasets is highly time consuming and expensive, which motivat…
Self-Supervision with Superpixels: Training Few-shot Medical Image Segmentation without Annotation
Cheng Ouyang, Carlo Biffi, Chen Chen +3
Few-shot semantic segmentation (FSS) has great potential for medical imaging applications. Most of the existing FSS techniques require abundant annotated semantic classes for train…
3D High-Resolution Cardiac Segmentation Reconstruction from 2D Views using Conditional Variational Autoencoders
Carlo Biffi, Juan J. Cerrolaza, Giacomo Tarroni +4
Accurate segmentation of heart structures imaged by cardiac MR is key for the quantitative analysis of pathology. High-resolution 3D MR sequences enable whole-heart structural imag…
Automatic 3D bi-ventricular segmentation of cardiac images by a shape-refined multi-task deep learning approach
Jinming Duan, Ghalib Bello, Jo Schlemper +7
Deep learning approaches have achieved state-of-the-art performance in cardiac magnetic resonance (CMR) image segmentation. However, most approaches have focused on learning image…
Learning Interpretable Anatomical Features Through Deep Generative Models: Application to Cardiac Remodeling
Carlo Biffi, Ozan Oktay, Giacomo Tarroni +9
Alterations in the geometry and function of the heart define well-established causes of cardiovascular disease. However, current approaches to the diagnosis of cardiovascular disea…