94 citations · 335 across the 76 of their papers we have counts for
11 papers · 2 filters
Maximum Entropy on Erroneous Predictions (MEEP): Improving model calibration for medical image segmentation
Agostina Larrazabal, Cesar Martinez, Jose Dolz +1
Modern deep neural networks achieved remarkable progress in medical image segmentation tasks. However, it has recently been observed that they tend to produce overconfident estimat…
A self-training framework for glaucoma grading in OCT B-scans
Gabriel García, Adrián Colomer, Rafael Verdú-Monedero +2
In this paper, we present a self-training-based framework for glaucoma grading using OCT B-scans under the presence of domain shift. Particularly, the proposed two-step learning me…
The Devil is in the Margin: Margin-based Label Smoothing for Network Calibration
Bingyuan Liu, Ismail Ben Ayed, Adrian Galdran +1
In spite of the dominant performances of deep neural networks, recent works have shown that they are poorly calibrated, resulting in over-confident predictions. Miscalibration can…
Source-Free Domain Adaptation for Image Segmentation
Mathilde Bateson, Hoel Kervadec, Jose Dolz +2
Domain adaptation (DA) has drawn high interest for its capacity to adapt a model trained on labeled source data to perform well on unlabeled or weakly labeled target data from a di…
Mutual-Information Based Few-Shot Classification
Malik Boudiaf, Ziko Imtiaz Masud, Jérôme Rony +3
We introduce Transductive Infomation Maximization (TIM) for few-shot learning. Our method maximizes the mutual information between the query features and their label predictions fo…
Beyond pixel-wise supervision for segmentation: A few global shape descriptors might be surprisingly good!
Hoel Kervadec, Houda Bahig, Laurent Letourneau-Guillon +2
Standard losses for training deep segmentation networks could be seen as individual classifications of pixels, instead of supervising the global shape of the predicted segmentation…