Search Results for author:
Found 10 papers, 6 papers with code
Reasoning with trees: interpreting CNNs using hierarchies
We introduce a framework that uses hierarchical segmentation techniques for faithful and interpretable explanations of Convolutional Neural Networks (CNNs).
Bridging Human Concepts and Computer Vision for Explainable Face Verification
With Artificial Intelligence (AI) influencing the decision-making process of sensitive applications such as Face Verification, it is fundamental to ensure the transparency, fairness, and accountability of decisions.
Transforming gradient-based techniques into interpretable methods
The explication of Convolutional Neural Networks (CNN) through xAI techniques often poses challenges in interpretation.
Unsupervised discovery of Interpretable Visual Concepts
Attribution maps from xAI techniques, such as Integrated Gradients, are a typical example of a visualization technique containing a high level of information, but with difficult interpretation.
BuyTheDips: PathLoss for improved topology-preserving deep learning-based image segmentation
Our method is an extension of the BALoss [1], in which we want to improve the leakage detection for better recovering the closeness property of the image segmentation.
Some equivalence relation between persistent homology and morphological dynamics
In Mathematical Morphology (MM), connected filters based on dynamics are used to filter the extrema of an image.
Local Intensity Order Transformation for Robust Curvilinear Object Segmentation
This results in a representation that preserves the inherent characteristic of the curvilinear structure while being robust to contrast changes.
QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results
In this study, we explore and evaluate a score developed during the BraTS 2019 and BraTS 2020 task on uncertainty quantification (QU-BraTS) and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation.
Deep learning for detection and segmentation of artefact and disease instances in gastrointestinal endoscopy
The Endoscopy Computer Vision Challenge (EndoCV) is a crowd-sourcing initiative to address eminent problems in developing reliable computer aided detection and diagnosis endoscopy systems and suggest a pathway for clinical translation of technologies.
VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images
Two datasets containing a total of 374 multi-detector CT scans from 355 patients were prepared and 4505 vertebrae have individually been annotated at voxel-level by a human-machine hybrid algorithm (https://osf. io/nqjyw/, https://osf. io/t98fz/).
