Search Results for author:
Found 36 papers, 8 papers with code
Deployment of Deep Learning Model in Real World Clinical Setting: A Case Study in Obstetric Ultrasound
Despite the rapid development of AI models in medical image analysis, their validation in real-world clinical settings remains limited.
Non-discrimination Criteria for Generative Language Models
Our results address the presence of occupational gender bias within such conversational language models.
Diffusion-based Iterative Counterfactual Explanations for Fetal Ultrasound Image Quality Assessment
Obstetric ultrasound image quality is crucial for accurate diagnosis and monitoring of fetal health.
Shortcut Learning in Medical Image Segmentation
Shortcut learning is a phenomenon where machine learning models prioritize learning simple, potentially misleading cues from data that do not generalize well beyond the training set.
Learning semantic image quality for fetal ultrasound from noisy ranking annotation
We introduce the notion of semantic image quality for applications where image quality relies on semantic requirements.
Interpreting Equivariant Representations
We show how not accounting for the inductive biases leads to decreased performance on downstream tasks, and vice versa, how accounting for inductive biases can be done effectively by using an invariant projection of the latent representations.
Fast Diffusion-Based Counterfactuals for Shortcut Removal and Generation
Via a novel inpainting-based modification we spatially limit the changes made with no extra inference step, encouraging the removal of spatially constrained shortcut features while ensuring that the shortcut-free counterfactuals preserve their remaining image features to a high degree.
FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare
This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare.
Are Sex-based Physiological Differences the Cause of Gender Bias for Chest X-ray Diagnosis?
In this work, we investigate the causes of gender bias in machine learning-based chest X-ray diagnosis.
Are demographically invariant models and representations in medical imaging fair?
Medical imaging models have been shown to encode information about patient demographics such as age, race, and sex in their latent representation, raising concerns about their potential for discrimination.
An Automatic Guidance and Quality Assessment System for Doppler Imaging of Umbilical Artery
Examination of the umbilical artery with Doppler ultrasonography is performed to investigate blood supply to the fetus through the umbilical cord, which is vital for the monitoring of fetal health.
That Label's Got Style: Handling Label Style Bias for Uncertain Image Segmentation
Segmentation uncertainty models predict a distribution over plausible segmentations for a given input, which they learn from the annotator variation in the training set.
Removing confounding information from fetal ultrasound images
In order to utilize the vast amounts of data available in these databases, we develop and validate a series of methods for minimizing the confounding effects of embedded text and calipers on deep learning algorithms designed for ultrasound, using standard plane classification as a test case.
Laplacian Segmentation Networks: Improved Epistemic Uncertainty from Spatial Aleatoric Uncertainty
To ensure robustness to such incorrect segmentations, we propose Laplacian Segmentation Networks (LSN) that jointly model epistemic (model) and aleatoric (data) uncertainty in image segmentation.
On (assessing) the fairness of risk score models
Further, we address how to assess the fairness of risk score models quantitatively, including a discussion of metric choices and meaningful statistical comparisons between groups.
I saw, I conceived, I concluded: Progressive Concepts as Bottlenecks
Concept bottleneck models (CBMs) include a bottleneck of human-interpretable concepts providing explainability and intervention during inference by correcting the predicted, intermediate concepts.
DTU-Net: Learning Topological Similarity for Curvilinear Structure Segmentation
We conduct experiments on a challenging multi-class ultrasound scan segmentation dataset as well as a well-known retinal imaging dataset.
Feature robustness and sex differences in medical imaging: a case study in MRI-based Alzheimer's disease detection
Instead, while logistic regression is fully robust to dataset composition, we find that CNN performance is generally improved for both male and female subjects when including more female subjects in the training dataset.
diffConv: Analyzing Irregular Point Clouds with an Irregular View
Standard spatial convolutions assume input data with a regular neighborhood structure.
Spot the Difference: Detection of Topological Changes via Geometric Alignment
As a first step towards solving such alignment problems, we propose an unsupervised algorithm for the detection of changes in image topology.
Graph2Graph Learning with Conditional Autoregressive Models
For such tasks, the main requirement for intermediate representations of the data is to maintain the structure needed for output, i. e., keeping classes separated or maintaining the order indicated by the regressor.
Quantifying Topology In Pancreatic Tubular Networks From Live Imaging 3D Microscopy
b) We provide a full deep-learning methodology for this difficult noisy task on time-series image data.
Semantic similarity metrics for learned image registration
We propose a semantic similarity metric for image registration.
Is segmentation uncertainty useful?
Probabilistic image segmentation encodes varying prediction confidence and inherent ambiguity in the segmentation problem.
DeepSim: Semantic similarity metrics for learned image registration
We propose a semantic similarity metric for image registration.
Medical Imaging with Deep Learning: MIDL 2019 -- Extended Abstract Track
This compendium gathers all the accepted extended abstracts from the Second International Conference on Medical Imaging with Deep Learning (MIDL 2019), held in London, UK, 8-10 July 2019.
A Formalization of The Natural Gradient Method for General Similarity Measures
The method uses the Kullback-Leibler divergence, corresponding infinitesimally to the Fisher-Rao metric, which is pulled back to the parameter space of a family of probability distributions.
(q,p)-Wasserstein GANs: Comparing Ground Metrics for Wasserstein GANs
We contribute to the WGAN literature by introducing the family of $(q, p)$-Wasserstein GANs, which allow the use of more general $p$-Wasserstein metrics for $p\geq 1$ in the GAN learning procedure.
Learning from graphs with structural variation
We study the effect of structural variation in graph data on the predictive performance of graph kernels.
Wrapped Gaussian Process Regression on Riemannian Manifolds
Gaussian process (GP) regression is a powerful tool in non-parametric regression providing uncertainty estimates.
Probabilistic Riemannian submanifold learning with wrapped Gaussian process latent variable models
Latent variable models (LVMs) learn probabilistic models of data manifolds lying in an \emph{ambient} Euclidean space.
Learning from uncertain curves: The 2-Wasserstein metric for Gaussian processes
We prove uniqueness of the barycenter of a population of GPs, as well as convergence of the metric and the barycenter of their finite-dimensional counterparts.
Geodesic Exponential Kernels: When Curvature and Linearity Conflict
However, we show that for spaces with conditionally negative definite distances the geodesic Laplacian kernel can be generalized while retaining positive definiteness.
Grassmann Averages for Scalable Robust PCA
We exploit that averages can be made robust to formulate the Robust Grassmann Average (RGA) as a form of robust PCA.
Scalable kernels for graphs with continuous attributes
While graphs with continuous node attributes arise in many applications, state-of-the-art graph kernels for comparing continuous-attributed graphs suffer from a high runtime complexity; for instance, the popular shortest path kernel scales as $\mathcal{O}(n^4)$, where $n$ is the number of nodes.
Geometric tree kernels: Classification of COPD from airway tree geometry
Methodological contributions: This paper introduces a family of kernels for analyzing (anatomical) trees endowed with vector valued measurements made along the tree.
