9.8k citations · 10.8k across the 28 of their papers we have counts for
32 papers
Localise to segment: crop to improve organ at risk segmentation accuracy
Abraham George Smith, Denis Kutnár, Ivan Richter Vogelius +2
Increased organ at risk segmentation accuracy is required to reduce cost and complications for patients receiving radiotherapy treatment. Some deep learning methods for the segment…
Transformer Utilization in Medical Image Segmentation Networks
Saikat Roy, Gregor Koehler, Michael Baumgartner +4
Owing to success in the data-rich domain of natural images, Transformers have recently become popular in medical image segmentation. However, the pairing of Transformers with convo…
MedNeXt: Transformer-driven Scaling of ConvNets for Medical Image Segmentation
Saikat Roy, Gregor Koehler, Constantin Ulrich +5
There has been exploding interest in embracing Transformer-based architectures for medical image segmentation. However, the lack of large-scale annotated medical datasets make achi…
Explicit Temporal Embedding in Deep Generative Latent Models for Longitudinal Medical Image Synthesis
Julian Schön, Raghavendra Selvan, Lotte Nygård +2
Medical imaging plays a vital role in modern diagnostics and treatment. The temporal nature of disease or treatment progression often results in longitudinal data. Due to the cost…
CRADL: Contrastive Representations for Unsupervised Anomaly Detection and Localization
Carsten T. Lüth, David Zimmerer, Gregor Koehler +4
Unsupervised anomaly detection in medical imaging aims to detect and localize arbitrary anomalies without requiring annotated anomalous data during training. Often, this is achieve…
Interpreting Latent Spaces of Generative Models for Medical Images using Unsupervised Methods
Julian Schön, Raghavendra Selvan, Jens Petersen
Generative models such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) play an increasingly important role in medical image analysis. The latent space…
Metrics reloaded: Recommendations for image analysis validation
Lena Maier-Hein, Annika Reinke, Patrick Godau +71
Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. Particularly in automatic biomedical image analysis, chosen…
Patch-based Medical Image Segmentation using Matrix Product State Tensor Networks
Raghavendra Selvan, Erik B Dam, Søren Alexander Flensborg +1
Tensor networks are efficient factorisations of high-dimensional tensors into a network of lower-order tensors. They have been most commonly used to model entanglement in quantum m…
Continuous-Time Deep Glioma Growth Models
Jens Petersen, Fabian Isensee, Gregor Köhler +9
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 t…
RootPainter3D: Interactive-machine-learning enables rapid and accurate contouring for radiotherapy
Abraham George Smith, Jens Petersen, Cynthia Terrones-Campos +5
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. We investigate th…
GP-ConvCNP: Better Generalization for Convolutional Conditional Neural Processes on Time Series Data
Jens Petersen, Gregor Köhler, David Zimmerer +3
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…
Common Limitations of Image Processing Metrics: A Picture Story
Annika Reinke, Minu D. Tizabi, Carole H. Sudre +90
While the importance of automatic image analysis is continuously increasing, recent meta-research revealed major flaws with respect to algorithm validation. Performance metrics are…
Segmenting two-dimensional structures with strided tensor networks
Raghavendra Selvan, Erik B Dam, Jens Petersen
Tensor networks provide an efficient approximation of operations involving high dimensional tensors and have been extensively used in modelling quantum many-body systems. More rece…
A Case for the Score: Identifying Image Anomalies using Variational Autoencoder Gradients
David Zimmerer, Jens Petersen, Simon A. A. Kohl +1
Through training on unlabeled data, anomaly detection has the potential to impact computer-aided diagnosis by outlining suspicious regions. Previous work on deep-learning-based ano…
High- and Low-level image component decomposition using VAEs for improved reconstruction and anomaly detection
David Zimmerer, Jens Petersen, Klaus Maier-Hein
Variational Auto-Encoders have often been used for unsupervised pretraining, feature extraction and out-of-distribution and anomaly detection in the medical field. However, VAEs of…
Unsupervised Anomaly Localization using Variational Auto-Encoders
David Zimmerer, Fabian Isensee, Jens Petersen +2
An assumption-free automatic check of medical images for potentially overseen anomalies would be a valuable assistance for a radiologist. Deep learning and especially Variational A…
Deep Probabilistic Modeling of Glioma Growth
Jens Petersen, Paul F. Jäger, Fabian Isensee +8
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 ass…
Automated Design of Deep Learning Methods for Biomedical Image Segmentation
Fabian Isensee, Paul F. Jäger, Simon A. A. Kohl +2
Biomedical imaging is a driver of scientific discovery and core component of medical care, currently stimulated by the field of deep learning. While semantic segmentation algorithm…
Segmentation of Roots in Soil with U-Net
Abraham George Smith, Jens Petersen, Raghavendra Selvan +1
Plant root research can provide a way to attain stress-tolerant crops that produce greater yield in a diverse array of conditions. Phenotyping roots in soil is often challenging du…
A cross-center smoothness prior for variational Bayesian brain tissue segmentation
Wouter M. Kouw, Silas N. Ørting, Jens Petersen +2
Suppose one is faced with the challenge of tissue segmentation in MR images, without annotators at their center to provide labeled training data. One option is to go to another med…
Context-encoding Variational Autoencoder for Unsupervised Anomaly Detection
David Zimmerer, Simon A. A. Kohl, Jens Petersen +2
Unsupervised learning can leverage large-scale data sources without the need for annotations. In this context, deep learning-based auto encoders have shown great potential in detec…
Graph Refinement based Airway Extraction using Mean-Field Networks and Graph Neural Networks
Raghavendra Selvan, Thomas Kipf, Max Welling +4
Graph refinement, or the task of obtaining subgraphs of interest from over-complete graphs, can have many varied applications. In this work, we extract trees or collection of sub-t…
Learning to quantify emphysema extent: What labels do we need?
Silas Nyboe Ørting, Jens Petersen, Laura H. Thomsen +2
Accurate assessment of pulmonary emphysema is crucial to assess disease severity and subtype, to monitor disease progression and to predict lung cancer risk. However, visual assess…
nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation
Fabian Isensee, Jens Petersen, Andre Klein +8
The U-Net was presented in 2015. With its straight-forward and successful architecture it quickly evolved to a commonly used benchmark in medical image segmentation. The adaptation…