5 citations · 8 across the 5 of their papers we have counts for
5 papers
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…
Trust your neighbours: Penalty-based constraints for model calibration
Balamurali Murugesan, Sukesh Adiga, Bingyuan Liu +3
Ensuring reliable confidence scores from deep networks is of pivotal importance in critical decision-making systems, notably in the medical domain. While recent literature on calib…
Attention-based Dynamic Subspace Learners for Medical Image Analysis
Sukesh Adiga, Jose Dolz, Herve Lombaert
Learning similarity is a key aspect in medical image analysis, particularly in recommendation systems or in uncovering the interpretation of anatomical data in images. Most existin…
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…
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…