1.4k citations · 3.4k across the 30 of their papers we have counts for
30 papers
Benchmarking Foundation Models for Renal Lesion Stratification in CT
Hartmut Häntze, Sarah de Boer, Myrthe Buser +7
The rapid proliferation of open-source medical foundation models (FMs) raises a practical question: how well do their pre-trained representations transfer to clinically relevant bu…
Leveraging Open-Source Large Language Models for Clinical Information Extraction in Resource-Constrained Settings
Luc Builtjes, Joeran Bosma, Mathias Prokop +2
Medical reports contain rich clinical information but are often unstructured and written in domain-specific language, posing challenges for information extraction. While proprietar…
Robust Renal Mass Segmentation on CT: A Validation Study of an AI-Based Framework
Sarah de Boer, Hartmut Häntze, Kiran Vaidhya Venkadesh +9
Renal mass segmentation has important potential to enhance the clinical workflow, especially in settings requiring quantitative assessments. Kidney volume could serve as an importa…
The ULS23 Challenge: a Baseline Model and Benchmark Dataset for 3D Universal Lesion Segmentation in Computed Tomography
M. J. J. de Grauw, E. Th. Scholten, E. J. Smit +4
Size measurements of tumor manifestations on follow-up CT examinations are crucial for evaluating treatment outcomes in cancer patients. Efficient lesion segmentation can speed up…
MRSegmentator: Multi-Modality Segmentation of 40 Classes in MRI and CT
Hartmut Häntze, Lina Xu, Christian J. Mertens +29
Purpose: To develop and evaluate a deep learning model for multi-organ segmentation of MRI scans. Materials and Methods: The model was trained on 1,200 manually annotated 3D axial…
Transfer learning from a sparsely annotated dataset of 3D medical images
Gabriel Efrain Humpire-Mamani, Colin Jacobs, Mathias Prokop +2
Transfer learning leverages pre-trained model features from a large dataset to save time and resources when training new models for various tasks, potentially enhancing performance…
Kidney abnormality segmentation in thorax-abdomen CT scans
Gabriel Efrain Humpire Mamani, Nikolas Lessmann, Ernst Th. Scholten +3
In this study, we introduce a deep learning approach for segmenting kidney parenchyma and kidney abnormalities to support clinicians in identifying and quantifying renal abnormalit…
Emphysema Subtyping on Thoracic Computed Tomography Scans using Deep Neural Networks
Weiyi Xie, Colin Jacobs, Jean-Paul Charbonnier +2
Accurate identification of emphysema subtypes and severity is crucial for effective management of COPD and the study of disease heterogeneity. Manual analysis of emphysema subtypes…
Lumbar spine segmentation in MR images: a dataset and a public benchmark
Jasper W. van der Graaf, Miranda L. van Hooff, Constantinus F. M. Buckens +6
This paper presents a large publicly available multi-center lumbar spine magnetic resonance imaging (MRI) dataset with reference segmentations of vertebrae, intervertebral discs (I…
Uncertainty-Aware Multiple-Instance Learning for Reliable Classification: Application to Optical Coherence Tomography
Coen de Vente, Bram van Ginneken, Carel B. Hoyng +2
Deep learning classification models for medical image analysis often perform well on data from scanners that were used during training. However, when these models are applied to da…
Understanding metric-related pitfalls in image analysis validation
Annika Reinke, Minu D. Tizabi, Michael Baumgartner +75
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 int…
Domain adaptation strategies for cancer-independent detection of lymph node metastases
Péter Bándi, Maschenka Balkenhol, Marcory van Dijk +3
Recently, large, high-quality public datasets have led to the development of convolutional neural networks that can detect lymph node metastases of breast cancer at the level of ex…
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…
Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning
Alessa Hering, Lasse Hansen, Tony C. W. Mok +50
Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed. However, only a few studies have comprehensively compared medi…
Robust Segmentation Models using an Uncertainty Slice Sampling Based Annotation Workflow
Grzegorz Chlebus, Andrea Schenk, Horst K. Hahn +2
Semantic segmentation neural networks require pixel-level annotations in large quantities to achieve a good performance. In the medical domain, such annotations are expensive, beca…
Deep Clustering Activation Maps for Emphysema Subtyping
Weiyi Xie, Colin Jacobs, Bram van Ginneken
We propose a deep learning clustering method that exploits dense features from a segmentation network for emphysema subtyping from computed tomography (CT) scans. Using dense featu…
Automated Estimation of Total Lung Volume using Chest Radiographs and Deep Learning
Ecem Sogancioglu, Keelin Murphy, Ernst Th. Scholten +3
Total lung volume is an important quantitative biomarker and is used for the assessment of restrictive lung diseases. In this study, we investigate the performance of several deep-…
Dense Regression Activation Maps For Lesion Segmentation in CT scans of COVID-19 patients
Weiyi Xie, Colin Jacobs, Jean-Paul Charbonnier +1
Automatic lesion segmentation on thoracic CT enables rapid quantitative analysis of lung involvement in COVID-19 infections. However, obtaining a large amount of voxel-level annota…
Adversarial Attack Vulnerability of Medical Image Analysis Systems: Unexplored Factors
Gerda Bortsova, Cristina González-Gonzalo, Suzanne C. Wetstein +9
Adversarial attacks are considered a potentially serious security threat for machine learning systems. Medical image analysis (MedIA) systems have recently been argued to be vulner…
Relational Modeling for Robust and Efficient Pulmonary Lobe Segmentation in CT Scans
Weiyi Xie, Colin Jacobs, Jean-Paul Charbonnier +1
Pulmonary lobe segmentation in computed tomography scans is essential for regional assessment of pulmonary diseases. Recent works based on convolution neural networks have achieved…
Iterative Augmentation of Visual Evidence for Weakly-Supervised Lesion Localization in Deep Interpretability Frameworks: Application to Color Fundus Images
Cristina González-Gonzalo, Bart Liefers, Bram van Ginneken +1
Interpretability of deep learning (DL) systems is gaining attention in medical imaging to increase experts' trust in the obtained predictions and facilitate their integration in cl…
Vertebra partitioning with thin-plate spline surfaces steered by a convolutional neural network
Nikolas Lessmann, Jelmer M. Wolterink, Majd Zreik +3
Thin-plate splines can be used for interpolation of image values, but can also be used to represent a smooth surface, such as the boundary between two structures. We present a meth…
Evaluation of a deep learning system for the joint automated detection of diabetic retinopathy and age-related macular degeneration
Cristina González-Gonzalo, Verónica Sánchez-Gutiérrez, Paula Hernández-Martínez +5
Purpose: To validate the performance of a commercially-available, CE-certified deep learning (DL) system, RetCAD v.1.3.0 (Thirona, Nijmegen, The Netherlands), for the joint automat…
Computer aided detection of tuberculosis on chest radiographs: An evaluation of the CAD4TB v6 system
Keelin Murphy, Shifa Salman Habib, Syed Mohammad Asad Zaidi +10
There is a growing interest in the automated analysis of chest X-Ray (CXR) as a sensitive and inexpensive means of screening susceptible populations for pulmonary tuberculosis. In…