5 citations · 7 across the 3 of their papers we have counts for
3 papers
cs.CV2023★ 5 cited
Anatomically-aware Uncertainty for Semi-supervised Image Segmentation
Sukesh Adiga, Jose Dolz, Herve Lombaert
Semi-supervised learning relaxes the need of large pixel-wise labeled datasets for image segmentation by leveraging unlabeled data. A prominent way to exploit unlabeled data is to…
cs.CV2022
Leveraging Labeling Representations in Uncertainty-based Semi-supervised Segmentation
Sukesh Adiga, Jose Dolz, Herve Lombaert
Semi-supervised segmentation tackles the scarcity of annotations by leveraging unlabeled data with a small amount of labeled data. A prominent way to utilize the unlabeled data is…
cs.CV2020★ 2 cited
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…