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Found 11 papers, 8 papers with code
Neighbor-Aware Calibration of Segmentation Networks with Penalty-Based Constraints
Ensuring reliable confidence scores from deep neural networks is of paramount significance in critical decision-making systems, particularly in real-world domains such as healthcare.
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.
Active learning for medical image segmentation with stochastic batches
The performance of learning-based algorithms improves with the amount of labelled data used for training.
TAAL: Test-time Augmentation for Active Learning in Medical Image Segmentation
This paper proposes Test-time Augmentation for Active Learning (TAAL), a novel semi-supervised active learning approach for segmentation that exploits the uncertainty information offered by data transformations.
Real-time simulation of viscoelastic tissue behavior with physics-guided deep learning
The use of the physics-guided loss function in a deep learning model has led to a better generalization in the prediction of deformations in unseen simulation cases.
Test-Time Adaptation with Shape Moments for Image Segmentation
In typical clinical settings, the source data is inaccessible and the target distribution is represented with a handful of samples: adaptation can only happen at test time on a few or even a single subject(s).
Manifold-aware Synthesis of High-resolution Diffusion from Structural Imaging
The physical and clinical constraints surrounding diffusion-weighted imaging (DWI) often limit the spatial resolution of the produced images to voxels up to 8 times larger than those of T1w images.
Source-Free Domain Adaptation for Image Segmentation
Our method yields comparable results to several state of the art adaptation techniques, despite having access to much less information, as the source images are entirely absent in our adaptation phase.
Cost-Sensitive Regularization for Diabetic Retinopathy Grading from Eye Fundus Images
Assessing the degree of disease severity in biomedical images is a task similar to standard classification but constrained by an underlying structure in the label space.
The Little W-Net That Could: State-of-the-Art Retinal Vessel Segmentation with Minimalistic Models
Our analysis demonstrates that the retinal vessel segmentation problem is far from solved when considering test images that differ substantially from the training data, and that this task represents an ideal scenario for the exploration of domain adaptation techniques.
Constrained domain adaptation for Image segmentation
We propose to adapt segmentation networks with a constrained formulation, which embeds domain-invariant prior knowledge about the segmentation regions.
