61 citations · 87 across the 8 of their papers we have counts for
12 papers
Cost-Sensitive Regularization for Diabetic Retinopathy Grading from Eye Fundus Images
Adrian Galdran, José Dolz, Hadi Chakor +2
Assessing the degree of disease severity in biomedical images is a task similar to standard classification but constrained by an underlying structure in the label space. Such a str…
The Little W-Net That Could: State-of-the-Art Retinal Vessel Segmentation with Minimalistic Models
Adrian Galdran, André Anjos, José Dolz +3
The segmentation of the retinal vasculature from eye fundus images represents one of the most fundamental tasks in retinal image analysis. Over recent years, increasingly complex a…
Joint Progressive Knowledge Distillation and Unsupervised Domain Adaptation
Le Thanh Nguyen-Meidine, Eric Granger, Madhu Kiran +2
Currently, the divergence in distributions of design and operational data, and large computational complexity are limiting factors in the adoption of CNNs in real-world application…
Bounding boxes for weakly supervised segmentation: Global constraints get close to full supervision
Hoel Kervadec, Jose Dolz, Shanshan Wang +2
We propose a novel weakly supervised learning segmentation based on several global constraints derived from box annotations. Particularly, we leverage a classical tightness prior t…
Semi-supervised few-shot learning for medical image segmentation
Abdur R Feyjie, Reza Azad, Marco Pedersoli +3
Recent years have witnessed the great progress of deep neural networks on semantic segmentation, particularly in medical imaging. Nevertheless, training high-performing models requ…
Manifold-driven Attention Maps for Weakly Supervised Segmentation
Sukesh Adiga, Jose Dolz, Herve Lombaert
Segmentation using deep learning has shown promising directions in medical imaging as it aids in the analysis and diagnosis of diseases. Nevertheless, a main drawback of deep model…