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Found 6 papers, 4 papers with code
Anatomically-aware Uncertainty for Semi-supervised Image Segmentation
This work proposes a novel method to estimate segmentation uncertainty by leveraging global information from the segmentation masks.
Trust your neighbours: Penalty-based constraints for model calibration
Ensuring reliable confidence scores from deep networks is of pivotal importance in critical decision-making systems, notably in the medical domain.
Attention-based Dynamic Subspace Learners for Medical Image Analysis
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.
Leveraging Labeling Representations in Uncertainty-based Semi-supervised Segmentation
The learnt labeling representation is used to map the prediction of the segmentation into a set of plausible masks.
Manifold-driven Attention Maps for Weakly Supervised Segmentation
Segmentation using deep learning has shown promising directions in medical imaging as it aids in the analysis and diagnosis of diseases.
FPD-M-net: Fingerprint Image Denoising and Inpainting Using M-Net Based Convolutional Neural Networks
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.
