34 citations · 113 across the 20 of their papers we have counts for
20 papers
A Neuroimaging Simulation Framework for Developing and Evaluating Causal AI
Eryn Libert-Scott, Emma A. M. Stanley, Vibujithan Vigneshwaran +3
Causally linking disease-related factors to image-derived biomarkers provides a powerful pathway to understanding disease mechanisms. Despite growing interest in applying causal ar…
Towards Brain MRI Foundation Models for the Clinic: Findings from the FOMO25 Challenge
Asbjørn Munk, Stefano Cerri, Vardan Nersesjan +81
Clinical deployment of automated brain MRI analysis faces a fundamental challenge: clinical data is heterogeneous and noisy, and high-quality labels are prohibitively costly to obt…
From Redaction to Restoration: Deep Learning for Medical Image Anonymization and Reconstruction
Adrienne Kline, Abhijit Gaonkar, Daniel Pittman +2
Removing patient-specific information from medical images is crucial to enable sharing and open science without compromising patient identities. However, many methods currently use…
CRAwDAD: Causal Reasoning Augmentation with Dual-Agent Debate
Finn G. Vamosi, Nils D. Forkert
When people reason about cause and effect, they often consider many competing "what if" scenarios before deciding which explanation fits best. Analogously, advanced language models…
Semi-disentangled spatiotemporal implicit neural representations of longitudinal neuroimaging data for trajectory classification
Agampreet Aulakh, Nils D. Forkert, Matthias Wilms
The human brain undergoes dynamic, potentially pathology-driven, structural changes throughout a lifespan. Longitudinal Magnetic Resonance Imaging (MRI) and other neuroimaging data…
Exploring the interplay of label bias with subgroup size and separability: A case study in mammographic density classification
Emma A. M. Stanley, Raghav Mehta, Mélanie Roschewitz +2
Systematic mislabelling affecting specific subgroups (i.e., label bias) in medical imaging datasets represents an understudied issue concerning the fairness of medical AI systems.…
Deep Learning for Retinal Degeneration Assessment: A Comprehensive Analysis of the MARIO Challenge
Rachid Zeghlache, Ikram Brahim, Pierre-Henri Conze +47
The MARIO challenge, held at MICCAI 2024, focused on advancing the automated detection and monitoring of age-related macular degeneration (AMD) through the analysis of optical cohe…
MACAW: A Causal Generative Model for Medical Imaging
Vibujithan Vigneshwaran, Erik Ohara, Matthias Wilms +1
Although deep learning techniques show promising results for many neuroimaging tasks in research settings, they have not yet found widespread use in clinical scenarios. One of the…
Towards objective and systematic evaluation of bias in artificial intelligence for medical imaging
Emma A. M. Stanley, Raissa Souza, Anthony Winder +4
Artificial intelligence (AI) models trained using medical images for clinical tasks often exhibit bias in the form of disparities in performance between subgroups. Since not all so…
Weakly Supervised Medical Image Segmentation With Soft Labels and Noise Robust Loss
Banafshe Felfeliyan, Abhilash Hareendranathan, Gregor Kuntze +4
Recent advances in deep learning algorithms have led to significant benefits for solving many medical image analysis problems. Training deep learning models commonly requires large…
Self-Supervised-RCNN for Medical Image Segmentation with Limited Data Annotation
Banafshe Felfeliyan, Abhilash Hareendranathan, Gregor Kuntze +4
Many successful methods developed for medical image analysis that are based on machine learning use supervised learning approaches, which often require large datasets annotated by…
Bone Adaptation as a Geometric Flow
Bryce A. Besler, Tannis D. Kemp, Nils D. Forkert +1
This paper presents bone adaptation as a geometric flow. The proposed method is based on two assumptions: first, that the bone surface is smooth (not fractal) permitting the defini…
Constructing High-Order Signed Distance Maps from Computed Tomography Data with Application to Bone Morphometry
Bryce A. Besler, Tannis D. Kemp, Nils D. Forkert +1
An algorithm is presented for constructing high-order signed distance fields for two phase materials imaged with computed tomography. The signed distance field is high-order in tha…
High-Order Signed Distance Transform of Sampled Signals
Bryce A. Besler, Tannis D. Kemp, Nils D. Forkert +1
Signed distance transforms of sampled signals can be constructed better than the traditional exact signed distance transform. Such a transform is termed the high-order signed dista…
Local Morphometry of Closed, Implicit Surfaces
Bryce A Besler, Tannis D. Kemp, Andrew S. Michalski +2
Anatomical structures such as the hippocampus, liver, and bones can be analyzed as orientable, closed surfaces. This permits the computation of volume, surface area, mean curvature…
Sphere Under Advection and Mean Curvature Flow
Bryce A. Besler, Tannis D. Kemp, Nils D. Forkert +1
Advection and mean curvature flow is used as a model of bone microarchitecture adaptation. It is an equivalent geometric flow to prescribed mean curvature flow with an additional r…
Bidirectional Modeling and Analysis of Brain Aging with Normalizing Flows
Matthias Wilms, Jordan J. Bannister, Pauline Mouches +4
Brain aging is a widely studied longitudinal process throughout which the brain undergoes considerable morphological changes and various machine learning approaches have been propo…
Artifacts of Quantization in Distance Transforms
Bryce A. Besler, Tannis D. Kemp, Nils D. Forkert +1
Distance transforms are a central tool in shape analysis, morphometry, and curve evolution problems. This work describes and investigates an artifact present in distance maps compu…
Design of a Head Coil for High Resolution Mouse Brain Perfusion Imaging using Magnetic Particle Imaging
Matthias Graeser, Peter Ludewig, Patryk Szwargulski +6
Magnetic Particle Imaging (MPI) is a novel and versatile imaging modality developing towards human application. When up-scaling to human size, the sensitivity of the systems natura…
DeepVesselNet: Vessel Segmentation, Centerline Prediction, and Bifurcation Detection in 3-D Angiographic Volumes
Giles Tetteh, Velizar Efremov, Nils D. Forkert +6
We present DeepVesselNet, an architecture tailored to the challenges faced when extracting vessel networks or trees and corresponding features in 3-D angiographic volumes using dee…