activity
20152020
most citedSemi-supervised few-shot learning for medical image segmentation

61 citations · 89 across the 8 of their papers we have counts for

collaborators

12 papers

cs.CV2020

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…

eess.IV20207 cited

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…

cs.CV20202 cited

A Flow-Guided Mutual Attention Network for Video-Based Person Re-Identification

Madhu Kiran, Amran Bhuiyan, Louis-Antoine Blais-Morin +3

Person Re-Identification (ReID) is a challenging problem in many video analytics and surveillance applications, where a person's identity must be associated across a distributed no…

cs.CV20201 cited

Medical Imaging with Deep Learning: MIDL 2020 -- Short Paper Track

Tal Arbel, Ismail Ben Ayed, Marleen de Bruijne +3

This compendium gathers all the accepted extended abstracts from the Third International Conference on Medical Imaging with Deep Learning (MIDL 2020), held in Montreal, Canada, 6-9…

cs.CV202017 cited

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…

cs.CV202061 cited

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…