1 code implementation • 24 Oct 2023 • Sukesh Adiga V, Jose Dolz, Herve Lombaert
This work proposes a novel method to estimate segmentation uncertainty by leveraging global information from the segmentation masks.
1 code implementation • 11 Mar 2023 • Balamurali Murugesan, Sukesh Adiga V, Bingyuan Liu, Hervé Lombaert, Ismail Ben Ayed, Jose Dolz
Ensuring reliable confidence scores from deep networks is of pivotal importance in critical decision-making systems, notably in the medical domain.
no code implementations • 18 Jun 2022 • Sukesh Adiga V, Jose Dolz, Herve Lombaert
This integrated attention mechanism provides a visual insight of discriminative image features that contribute to the clustering of image sets and a visual explanation of the embedding features.
1 code implementation • 10 Mar 2022 • Sukesh Adiga V, Jose Dolz, Herve Lombaert
The learnt labeling representation is used to map the prediction of the segmentation into a set of plausible masks.
no code implementations • 7 Apr 2020 • Sukesh Adiga V, 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.
1 code implementation • 26 Dec 2018 • Sukesh Adiga V, Jayanthi Sivaswamy
Our architecture is based on the M-net with a change: structure similarity loss function, used for better extraction of the fingerprint from the noisy background.