Search Results for author:
Found 34 papers, 15 papers with code
MedNeXt: Transformer-driven Scaling of ConvNets for Medical Image Segmentation
This leads to state-of-the-art performance on 4 tasks on CT and MRI modalities and varying dataset sizes, representing a modernized deep architecture for medical image segmentation.
Understanding metric-related pitfalls in image analysis validation
Validation metrics are key for the reliable tracking of scientific progress and for bridging the current chasm between artificial intelligence (AI) research and its translation into practice.
Explicit Temporal Embedding in Deep Generative Latent Models for Longitudinal Medical Image Synthesis
This, in contrast to previous methods, allows us to synthesize continuous, smooth, and high-quality longitudinal volumetric data with limited supervision.
A Residual Diffusion Model for High Perceptual Quality Codec Augmentation
Diffusion probabilistic models have recently achieved remarkable success in generating high quality image and video data.
CRADL: Contrastive Representations for Unsupervised Anomaly Detection and Localization
By utilizing the representations of contrastive learning, we aim to fix the over-fixation on low-level features and learn more semantic-rich representations.
Interpreting Latent Spaces of Generative Models for Medical Images using Unsupervised Methods
The results show that unsupervised methods to discover interpretable directions in GANs generalize to VAEs and can be applied to medical images.
Metrics reloaded: Pitfalls and recommendations for image analysis validation
The framework was developed in a multi-stage Delphi process and is based on the novel concept of a problem fingerprint - a structured representation of the given problem that captures all aspects that are relevant for metric selection, from the domain interest to the properties of the target structure(s), data set and algorithm output.
Patch-based Medical Image Segmentation using Matrix Product State Tensor Networks
The performance of the proposed tensor network segmentation model is compared with relevant baseline methods.
Continuous-Time Deep Glioma Growth Models
The ability to estimate how a tumor might evolve in the future could have tremendous clinical benefits, from improved treatment decisions to better dose distribution in radiation therapy.
RootPainter3D: Interactive-machine-learning enables rapid and accurate contouring for radiotherapy
Organ-at-risk contouring is still a bottleneck in radiotherapy, with many deep learning methods falling short of promised results when evaluated on clinical data.
GP-ConvCNP: Better Generalization for Convolutional Conditional Neural Processes on Time Series Data
Neural Processes (NPs) are a family of conditional generative models that are able to model a distribution over functions, in a way that allows them to perform predictions at test time conditioned on a number of context points.
Common Limitations of Image Processing Metrics: A Picture Story
While the importance of automatic image analysis is continuously increasing, recent meta-research revealed major flaws with respect to algorithm validation.
Segmenting two-dimensional structures with strided tensor networks
We use the matrix product state (MPS) tensor network on non-overlapping patches of a given input image to predict the segmentation mask by learning a pixel-wise linear classification rule in a high dimensional space.
Frequency Decomposition in Neural Processes
Neural Processes are a powerful tool for learning representations of function spaces purely from examples, in a way that allows them to perform predictions at test time conditioned on so-called context observations.
A Case for the Score: Identifying Image Anomalies using Variational Autoencoder Gradients
Through training on unlabeled data, anomaly detection has the potential to impact computer-aided diagnosis by outlining suspicious regions.
High- and Low-level image component decomposition using VAEs for improved reconstruction and anomaly detection
Variational Auto-Encoders have often been used for unsupervised pretraining, feature extraction and out-of-distribution and anomaly detection in the medical field.
Deep Probabilistic Modeling of Glioma Growth
Existing approaches to modeling the dynamics of brain tumor growth, specifically glioma, employ biologically inspired models of cell diffusion, using image data to estimate the associated parameters.
Unsupervised Anomaly Localization using Variational Auto-Encoders
An assumption-free automatic check of medical images for potentially overseen anomalies would be a valuable assistance for a radiologist.
Automated Design of Deep Learning Methods for Biomedical Image Segmentation
Biomedical imaging is a driver of scientific discovery and core component of medical care, currently stimulated by the field of deep learning.
A cross-center smoothness prior for variational Bayesian brain tissue segmentation
Here we present a smoothness prior that is fit to segmentations produced at another medical center.
Segmentation of Roots in Soil with U-Net
We evaluate our system using 867 images for which we have obtained line-intersect counts, attaining a Spearman rank correlation of 0. 9748 and an $r^2$ of 0. 9217.
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.
Context-encoding Variational Autoencoder for Unsupervised Anomaly Detection
Unsupervised learning can leverage large-scale data sources without the need for annotations.
Graph Refinement based Airway Extraction using Mean-Field Networks and Graph Neural Networks
Graph refinement, or the task of obtaining subgraphs of interest from over-complete graphs, can have many varied applications.
Learning to quantify emphysema extent: What labels do we need?
We evaluate performance on 600 low-dose CT scans from the Danish Lung Cancer Screening Trial and find that learning from emphysema presence labels, which are much easier to obtain, gives equally good performance to learning from emphysema extent labels.
nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation
The U-Net was presented in 2015.
Ranked #3 on
Medical Image Segmentation
on Synapse multi-organ CT
Extracting Tree-structures in CT data by Tracking Multiple Statistically Ranked Hypotheses
We propose to use statistical ranking of local hypotheses in constructing the MHT tree, which yields a probabilistic interpretation of scores across scales and helps alleviate the scale-dependence of MHT parameters.
Feature learning based on visual similarity triplets in medical image analysis: A case study of emphysema in chest CT scans
We evaluate the networks on 973 images, and show that the CNNs can learn disease relevant feature representations from derived similarity triplets.
Extraction of Airways using Graph Neural Networks
We present extraction of tree structures, such as airways, from image data as a graph refinement task.
Mean Field Network based Graph Refinement with application to Airway Tree Extraction
Performance of the method is compared with two methods: the first uses probability images from a trained voxel classifier with region growing, which is similar to one of the best performing methods at EXACT'09 airway challenge, and the second method is based on Bayesian smoothing on these probability images.
Extraction of Airways with Probabilistic State-space Models and Bayesian Smoothing
The evolution of individual branches is modelled using a process model and the observed data is incorporated into the update step of the Bayesian smoother using a measurement model that is based on a multi-scale blob detector.
Extraction of airway trees using multiple hypothesis tracking and template matching
The results show improvements in performance when compared to the original method and region growing on intensity images.
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.
