Search Results for author:
Found 57 papers, 29 papers with code
Mamba? Catch The Hype Or Rethink What Really Helps for Image Registration
Our findings indicate that adopting "advanced" computational elements fails to significantly improve registration accuracy.
Stable-Pose: Leveraging Transformers for Pose-Guided Text-to-Image Generation
Controllable text-to-image (T2I) diffusion models have shown impressive performance in generating high-quality visual content through the incorporation of various conditions.
PASTA: Pathology-Aware MRI to PET Cross-Modal Translation with Diffusion Models
Our qualitative and quantitative results confirm that the synthesized PET scans from PASTA not only reach the best quantitative scores but also preserve the pathology correctly.
From Barlow Twins to Triplet Training: Differentiating Dementia with Limited Data
To address these issues, we propose Triplet Training for differential diagnosis with limited target data.
Stochastic Cortical Self-Reconstruction
Magnetic resonance imaging (MRI) is critical for diagnosing neurodegenerative diseases, yet accurately assessing mild cortical atrophy remains a challenge due to its subtlety.
V2C-Long: Longitudinal Cortex Reconstruction with Spatiotemporal Correspondence
In contrast to existing methods, V2C-Long surfaces are directly comparable in a cross-sectional and longitudinal manner.
Neural deformation fields for template-based reconstruction of cortical surfaces from MRI
The reconstruction of cortical surfaces is a prerequisite for quantitative analyses of the cerebral cortex in magnetic resonance imaging (MRI).
H-ViT: A Hierarchical Vision Transformer for Deformable Image Registration
This paper introduces a novel top-down representation approach for deformable image registration which estimates the deformation field by capturing various short- and long-range flow features at different scale levels.
Keep the Faith: Faithful Explanations in Convolutional Neural Networks for Case-Based Reasoning
During inference, similarities of latent features to prototypes are linearly classified to form predictions and attribution maps are provided to explain the similarity.
RegBN: Batch Normalization of Multimodal Data with Regularization
The proposed method demonstrates broad applicability across different architectures such as multilayer perceptrons, convolutional neural networks, and vision transformers, enabling effective normalization of both low- and high-level features in multimodal neural networks.
MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision
For the medical domain, we present a large collection of anatomical shapes (e. g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems.
Abdominal organ segmentation via deep diffeomorphic mesh deformations
However, the generalization of these deep learning-based approaches to different organs and datasets, a crucial property for deployment in clinical environments, has not yet been assessed.
HALOS: Hallucination-free Organ Segmentation after Organ Resection Surgery
The wide range of research in deep learning-based medical image segmentation pushed the boundaries in a multitude of applications.
Don't PANIC: Prototypical Additive Neural Network for Interpretable Classification of Alzheimer's Disease
We propose PANIC, a prototypical additive neural network for interpretable AD classification that integrates 3D image and tabular data.
Joint Reconstruction and Parcellation of Cortical Surfaces
The reconstruction of cerebral cortex surfaces from brain MRI scans is instrumental for the analysis of brain morphology and the detection of cortical thinning in neurodegenerative diseases like Alzheimer's disease (AD).
CASHformer: Cognition Aware SHape Transformer for Longitudinal Analysis
To this end, we introduce CASHformer, a transformer-based framework to model longitudinal shape trajectories in AD.
Is a PET all you need? A multi-modal study for Alzheimer's disease using 3D CNNs
AD classification and focus on differential diagnosis of dementia, where fusing multi-modal image information conforms with a clinical need.
Vox2Cortex: Fast Explicit Reconstruction of Cortical Surfaces from 3D MRI Scans with Geometric Deep Neural Networks
The reconstruction of cortical surfaces from brain magnetic resonance imaging (MRI) scans is essential for quantitative analyses of cortical thickness and sulcal morphology.
TransforMesh: A Transformer Network for Longitudinal modeling of Anatomical Meshes
To the best of our knowledge, this is the first work that combines transformer and mesh networks.
Alzheimer's Disease Diagnosis via Deep Factorization Machine Models
The current state-of-the-art deep neural networks (DNNs) for Alzheimer's Disease diagnosis use different biomarker combinations to classify patients, but do not allow extracting knowledge about the interactions of biomarkers.
Scalable, Axiomatic Explanations of Deep Alzheimer's Diagnosis from Heterogeneous Data
We propose Shapley Value Explanation of Heterogeneous Neural Networks (SVEHNN) for explaining the Alzheimer's diagnosis made by a DNN from the 3D point cloud of the neuroanatomy and tabular biomarkers.
Combining 3D Image and Tabular Data via the Dynamic Affine Feature Map Transform
Prior work on diagnosing Alzheimer's disease from magnetic resonance images of the brain established that convolutional neural networks (CNNs) can leverage the high-dimensional image information for classifying patients.
The Medical Segmentation Decathlon
Segmentation is so far the most widely investigated medical image processing task, but the various segmentation challenges have typically been organized in isolation, such that algorithm development was driven by the need to tackle a single specific clinical problem.
STRUDEL: Self-Training with Uncertainty Dependent Label Refinement across Domains
We propose to predict the uncertainty of pseudo labels and integrate it in the training process with an uncertainty-guided loss function to highlight labels with high certainty.
Geometric Deep Learning on Anatomical Meshes for the Prediction of Alzheimer's Disease
Geometric deep learning can find representations that are optimal for a given task and therefore improve the performance over pre-defined representations.
Recalibration of Neural Networks for Point Cloud Analysis
First, we demonstrate the benefit and versatility of our proposed modules by incorporating them into three state-of-the-art networks for 3D point cloud analysis: PointNet++, DGCNN, and RSCNN.
Semi-Structured Deep Piecewise Exponential Models
We propose a versatile framework for survival analysis that combines advanced concepts from statistics with deep learning.
Discriminative and Generative Models for Anatomical Shape Analysison Point Clouds with Deep Neural Networks
The key insights are that (i) learning a shape representation specific to the given task yields higher performance than alternative shape descriptors, (ii) multi-structure analysis is both more efficient and more accurate than single-structure analysis, and (iii) point clouds generated by our model capture morphological differences associated to Alzheimers disease, to the point that they can be used to train a discriminative model for disease classification.
Estimation of Causal Effects in the Presence of Unobserved Confounding in the Alzheimer's Continuum
We derive a causal graph from the current clinical knowledge on cause and effect in the Alzheimer's disease continuum, and show that identifiability of the causal effect requires all confounders to be known and measured.
Importance Driven Continual Learning for Segmentation Across Domains
The ability of neural networks to continuously learn and adapt to new tasks while retaining prior knowledge is crucial for many applications.
Recalibrating 3D ConvNets with Project & Excite
Fully Convolutional Neural Networks (F-CNNs) achieve state-of-the-art performance for segmentation tasks in computer vision and medical imaging.
Detect and Correct Bias in Multi-Site Neuroimaging Datasets
Given such evidence, we take a closer look at confounding bias, which is often viewed as the main shortcoming in observational studies.
A Wide and Deep Neural Network for Survival Analysis from Anatomical Shape and Tabular Clinical Data
Our network is trained end-to-end to combine information from a patient's hippocampus shape and clinical biomarkers.
Quantifying Confounding Bias in Neuroimaging Datasets with Causal Inference
In this work, we combine 12, 207 MRI scans from 15 studies and show that simple pooling is often ill-advised due to introducing various types of biases in the training data.
`Project & Excite' Modules for Segmentation of Volumetric Medical Scans
Fully Convolutional Neural Networks (F-CNNs) achieve state-of-the-art performance for image segmentation in medical imaging.
Adversarial Learned Molecular Graph Inference and Generation
Recent methods for generating novel molecules use graph representations of molecules and employ various forms of graph convolutional neural networks for inference.
BrainTorrent: A Peer-to-Peer Environment for Decentralized Federated Learning
A disadvantage of FL is the dependence on a central server, which requires all clients to agree on one trusted central body, and whose failure would disrupt the training process of all clients.
'Squeeze & Excite' Guided Few-Shot Segmentation of Volumetric Images
This representation is passed on to the segmenter arm that uses this information to segment the new query image.
Data Augmentation with Manifold Exploring Geometric Transformations for Increased Performance and Robustness
Compared with traditional augmentation methods, and with images synthesized by Generative Adversarial Networks our method not only achieves state-of-the-art performance but also significantly improves the network's robustness.
The Liver Tumor Segmentation Benchmark (LiTS)
In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018.
Bayesian QuickNAT: Model Uncertainty in Deep Whole-Brain Segmentation for Structure-wise Quality Control
Next to voxel-wise uncertainty, we introduce four metrics to quantify structure-wise uncertainty in segmentation for quality control.
InfiNet: Fully Convolutional Networks for Infant Brain MRI Segmentation
We present a novel, parameter-efficient and practical fully convolutional neural network architecture, termed InfiNet, aimed at voxel-wise semantic segmentation of infant brain MRI images at iso-intense stage, which can be easily extended for other segmentation tasks involving multi-modalities.
Recalibrating Fully Convolutional Networks with Spatial and Channel 'Squeeze & Excitation' Blocks
Towards this end, we introduce three variants of SE modules for segmentation, (i) squeezing spatially and exciting channel-wise, (ii) squeezing channel-wise and exciting spatially and (iii) joint spatial and channel 'squeeze & excitation'.
Deep Shape Analysis on Abdominal Organs for Diabetes Prediction
Morphological analysis of organs based on images is a key task in medical imaging computing.
Keypoint Transfer for Fast Whole-Body Segmentation
We introduce an approach for image segmentation based on sparse correspondences between keypoints in testing and training images.
Deep Multi-Structural Shape Analysis: Application to Neuroanatomy
We propose a deep neural network for supervised learning on neuroanatomical shapes.
Detect, Quantify, and Incorporate Dataset Bias: A Neuroimaging Analysis on 12,207 Individuals
Next, we introduce metrics to quantify the compatibility across datasets and to create embeddings of neuroimaging sites.
Inherent Brain Segmentation Quality Control from Fully ConvNet Monte Carlo Sampling
We introduce inherent measures for effective quality control of brain segmentation based on a Bayesian fully convolutional neural network, using model uncertainty.
Gaussian Process Uncertainty in Age Estimation as a Measure of Brain Abnormality
Finally, we illustrate differences in the disease pattern to normal aging, supporting the application of uncertainty as a measure of neuropathology.
Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional Networks
Fully convolutional neural networks (F-CNNs) have set the state-of-the-art in image segmentation for a plethora of applications.
QuickNAT: A Fully Convolutional Network for Quick and Accurate Segmentation of Neuroanatomy
We introduce QuickNAT, a fully convolutional, densely connected neural network that segments a \revision{MRI brain scan} in 20 seconds.
A Multi-Armed Bandit to Smartly Select a Training Set from Big Medical Data
With the availability of big medical image data, the selection of an adequate training set is becoming more important to address the heterogeneity of different datasets.
Error Corrective Boosting for Learning Fully Convolutional Networks with Limited Data
While large datasets of unlabeled image data are available in medical applications, access to manually labeled data is very limited.
ReLayNet: Retinal Layer and Fluid Segmentation of Macular Optical Coherence Tomography using Fully Convolutional Network
Optical coherence tomography (OCT) is used for non-invasive diagnosis of diabetic macular edema assessing the retinal layers.
DeepNAT: Deep Convolutional Neural Network for Segmenting Neuroanatomy
We introduce DeepNAT, a 3D Deep convolutional neural network for the automatic segmentation of NeuroAnaTomy in T1-weighted magnetic resonance images.
Diverse Landmark Sampling from Determinantal Point Processes for Scalable Manifold Learning
High computational costs of manifold learning prohibit its application for large point sets.
Sparse Projections of Medical Images onto Manifolds
To this end, out-of-sample extensions are applied by constructing an interpolation function that maps from the input space to the low-dimensional manifold.
