13 results
Now showing1 - 10 of 13
- Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Diffusion models with ensembled structure-based anomaly scoring for unsupervised anomaly detectionSupervised deep learning techniques show promise in medical image analysis. However, they require comprehensive annotated data sets, which poses challenges, particularly for rare diseases. Consequently, unsupervised anomaly detection (UAD) emerges as a viable alternative for pathology segmentation, as only healthy data is required for training. However, recent UAD anomaly scoring functions often focus on intensity only and neglect structural differences, which impedes the segmentation performance. This work investigates the potential of Structural Similarity (SSIM) to bridge this gap. SSIM captures both intensity and structural disparities and can be advantageous over the classical l1 error. However, we show that there is more than one optimal kernel size for the SSIM calculation for different pathologies. Therefore, we investigate an adaptive ensembling strategy for various kernel sizes to offer a more pathology-agnostic scoring mechanism. We demonstrate that this ensembling strategy can enhance the performance of DMs and mitigate the sensitivity to different kernel sizes across varying pathologies, highlighting its promise for brain MRI anomaly detection.Publicationtype: Conference Paper10 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Quantitative evaluation of activation maps for weakly-supervised lung nodule segmentation(SPIE, 2024-01); ; ; ; The manual assessment of chest radiographs by radiologists is a time-consuming and error-prone process that relies on the availability of trained professionals. Deep learning methods have the potential to alleviate the workload of radiologists in pathology detection and diagnosis. However, one major drawback of deep learning methods is their lack of explainable decision-making, which is crucial in computer-aided diagnosis. To address this issue, activation maps of the underlying convolutional neural networks (CNN) are frequently used to indicate the regions of focus for the network during predictions. However, often, an evaluation of these activation maps concerning the actual predicted pathology is missing. In this study, we quantitatively evaluate the usage of activation maps for segmenting pulmonary nodules in chest radiographs. We compare transformer-based, CNN-based, and hybrid architectures using different visualization methods. Our results show that although high performance can be achieved in the classification task across all models, the activation masks show little correlation with the actual position of the nodules.Publicationtype: Conference Paper12 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Leveraging the Mahalanobis Distance to Enhance Unsupervised Brain MRI Anomaly Detection(Springer, 2024); ; ; ; Unsupervised Anomaly Detection (UAD) methods rely on healthy data distributions to identify anomalies as outliers. In brain MRI, a common approach is reconstruction-based UAD, where generative models reconstruct healthy brain MRIs, and anomalies are detected as deviations between input and reconstruction. However, this method is sensitive to imperfect reconstructions, leading to false positives that impede the segmentation. To address this limitation, we construct multiple reconstructions with probabilistic diffusion models. We then analyze the resulting distribution of these reconstructions using the Mahalanobis distance to identify anomalies as outliers. By leveraging information about normal variations and covariance of individual pixels within this distribution, we effectively refine anomaly scoring, leading to improved segmentation. Our experimental results demonstrate substantial performance improvements across various data sets. Specifically, compared to relying solely on single reconstructions, our approach achieves relative improvements of 15.9%, 35.4%, 48.0%, and 4.7% in terms of AUPRC for the BRATS21, ATLAS, MSLUB and WMH data sets, respectively.Publicationtype: Conference Paper30 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Nodule detection in chest radiographs with unsupervised pre-trained detection transformers(IEEE, 2023-09-01); ; ; ; The detection of pulmonary nodules in chest x-rays is important for early observation and monitoring of lung cancer which is a major reason for death worldwide. However, detecting nodules from x-rays is challenging as nodules are easily overseen by radiologists. Convolutional neural networks (CNN) show promising results in supporting the clinical practice by automatic lung nodule detection and localization. Recently, attention-based Vision Transformers have been successfully applied in computer vision tasks. For object detection, end-to-end solutions have been proposed that reduce the amount of encoded prior knowledge and manual postprocessing. This is desirable particularly for medical applications where data domains often vary.In this work, we evaluate the application of Detection Transformers for nodule detection in chest x-rays and compare them against four CNN-based baseline object detection algorithms. To overcome the data inefficiency of Vision Transformers, we investigate the use of self-supervision from large-scale data sources. Our results demonstrate the high performance of transformer-based object detectors, by consistently outperforming CNN-based baselines on the Node21 data set. Furthermore, we demonstrate that self-supervision improves the detection performance without the costly requirement of collecting annotated data.Publicationtype: Conference Paper23 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Patched diffusion models for unsupervised anomaly detection in brain MRIThe use of supervised deep learning techniques to detect pathologies in brain MRI scans can be challenging due to the diversity of brain anatomy and the need for annotated data sets. An alternative approach is to use unsupervised anomaly detection, which only requires sample-level labels of healthy brains to create a reference representation. This reference representation can then be compared to unhealthy brain anatomy in a pixel-wise manner to identify abnormalities. To accomplish this, generative models are needed to create anatomically consistent MRI scans of healthy brains. While recent diffusion models have shown promise in this task, accurately generating the complex structure of the human brain remains a challenge. In this paper, we propose a method that reformulates the generation task of diffusion models as a patch-based estimation of healthy brain anatomy, using spatial context to guide and improve reconstruction. We evaluate our approach on data of tumors and multiple sclerosis lesions and demonstrate a relative improvement of 25.1% compared to existing baselinesPublicationtype: Conference Paper34 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Patched Diffusion Models for Unsupervised Anomaly Detection in Brain MRIThe use of supervised deep learning techniques to detect pathologies in brain MRI scans can be challenging due to the diversity of brain anatomy and the need for annotated data sets. An alternative approach is to use unsupervised anomaly detection, which only requires sample-level labels of healthy brains to create a reference representation. This reference representation can then be compared to unhealthy brain anatomy in a pixel-wise manner to identify abnormalities. To accomplish this, generative models are needed to create anatomically consistent MRI scans of healthy brains. While recent diffusion models have shown promise in this task, accurately generating the complex structure of the human brain remains a challenge. In this paper, we propose a method that reformulates the generation task of diffusion models as a patch-based estimation of healthy brain anatomy, using spatial context to guide and improve reconstruction. We evaluate our approach on data of tumors and multiple sclerosis lesions and demonstrate a relative improvement of 25.1% compared to existing baselines.Publicationtype: Conference Paper21 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Infratentorial lesions in multiple sclerosis patients: intra- and inter-rater variability in comparison to a fully automated segmentation using 3D convolutional neural networks(Springer, 2022-04); ; ; ; Objective: Automated quantification of infratentorial multiple sclerosis lesions on magnetic resonance imaging is clinically relevant but challenging. To overcome some of these problems, we propose a fully automated lesion segmentation algorithm using 3D convolutional neural networks (CNNs). Methods: The CNN was trained on a FLAIR image alone or on FLAIR and T1-weighted images from 1809 patients acquired on 156 different scanners. An additional training using an extra class for infratentorial lesions was implemented. Three experienced raters manually annotated three datasets from 123 MS patients from different scanners. Results: The inter-rater sensitivity (SEN) was 80% for supratentorial lesions but only 62% for infratentorial lesions. There was no statistically significant difference between the inter-rater SEN and the SEN of the CNN with respect to the raters. For supratentorial lesions, the CNN featured an intra-rater intra-scanner SEN of 0.97 (R1 = 0.90, R2 = 0.84) and for infratentorial lesion a SEN of 0.93 (R1 = 0.61, R2 = 0.73). Conclusion: The performance of the CNN improved significantly for infratentorial lesions when specifically trained on infratentorial lesions using a T1 image as an additional input and matches the detection performance of experienced raters. Furthermore, for infratentorial lesions the CNN was more robust against repeated scans than experienced raters. Key Points: • A 3D convolutional neural network was trained on MRI data from 1809 patients (156 different scanners) for the quantification of supratentorial and infratentorial multiple sclerosis lesions. • Inter-rater variability was higher for infratentorial lesions than for supratentorial lesions. The performance of the 3D convolutional neural network (CNN) improved significantly for infratentorial lesions when specifically trained on infratentorial lesions using a T1 image as an additional input. • The detection performance of the CNN matches the detection performance of experienced raters.Publicationtype: Journal Article80 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Unsupervised anomaly detection in 3D brain MRI using deep learning with multi-task brain age predictionLesion detection in brain Magnetic Resonance Images (MRIs) remains a challenging task. MRIs are typically read and interpreted by domain experts, which is a tedious and time-consuming process. Recently, unsupervised anomaly detection (UAD) in brain MRI with deep learning has shown promising results to provide a quick, initial assessment. So far, these methods only rely on the visual appearance of healthy brain anatomy for anomaly detection. Another biomarker for abnormal brain development is the deviation between the brain age and the chronological age, which is unexplored in combination with UAD. We propose deep learning for UAD in 3D brain MRI considering additional age information. We analyze the value of age information during training, as an additional anomaly score, and systematically study several architecture concepts. Based on our analysis, we propose a novel deep learning approach for UAD with multi-task age prediction. We use clinical T1-weighted MRIs of 1735 healthy subjects and the publicly available BraTs 2019 data set for our study. Our novel approach significantly improves UAD performance with an AUC of 92.60% compared to an AUC-score of 84.37% using previous approaches without age information.Publicationtype: Conference Paper34 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Unsupervised anomaly detection in 3D brain MRI using deep learning with impured training data(2022-03); ; ; ; The detection of lesions in magnetic resonance imaging (MRI)-scans of human brains remains challenging, time-consuming and error-prone. Recently, unsupervised anomaly detection (UAD) methods have shown promising results for this task. These methods rely on training data sets that solely contain healthy samples. Compared to supervised approaches, this significantly reduces the need for an extensive amount of labeled training data. However, data labelling remains error-prone. We study how unhealthy samples within the training data affect anomaly detection performance for brain MRI-scans. For our evaluations, we consider autoencoders (AE) as a well-established baseline method for UAD. We systematically evaluate the effect of impured training data by injecting different quantities of unhealthy samples to our training set of healthy samples. We evaluate a method to identify falsely labeled samples directly during training based on the reconstruction error of the AE. Our results show that training with impured data decreases the UAD performance notably even with few falsely labeled samples. By performing outlier removal directly during training based on the reconstruction-loss, we demonstrate that falsely labeled data can be detected and that this mitigates the effect of falsely labeled data.Publicationtype: Conference Paper82 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Capturing Inter-Slice Dependencies of 3D Brain MRI-Scans for Unsupervised Anomaly DetectionThe increasing workloads for radiologists in clinical practice lead to the need for an automatic support tool for anomaly detection in brain MRI-scans. While supervised learning methods can detect and localize lesions in brain MRI-scans, the need for large, balanced data sets with pixel-level annotations limits their use. In contrast, unsupervised anomaly detection (UAD) models only require healthy brain data for training. Despite the inherent 3D structure of brain MRI-scans, most UAD studies focus on slice-wise processing. In this work, we capture the inter-slice dependencies of the human brain using recurrent neural networks (RNN) and transformer-based self-attention mechanisms together with variational autoencoders (VAE). We show that by this we can improve both reconstruction quality and UAD performance while the number of parameters remain similar to the 2D approach where the slices are processed individually.Publicationtype: Conference Paper61