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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 , Can complex-valued neural networks improve force sensing with optical coherence tomography?(IEEE, 2024-05); ; ; ; Deep learning has proven highly effective in processing optical coherence tomography (OCT) images. However, current approaches often treat complex OCT data as real-valued representations and mostly focus on the amplitude component. In contrast, OCT phase can detect submicrometer displacements, surpassing the resolution of amplitude-based methods. In this study, we investigate complex-valued and real-valued neural networks (cvNNs and rvNNs) for end-to-end needle tip force estimation from complex OCT data. We consider predicting absolute forces and force differences over time. We compare cvNNs with two rvNNs where amplitude and phase are either stacked in the channel dimension or processed separately in a dual-path architecture. Our results show that the amplitude-focused estimation of the absolute force is still best achieved with rvNNs. However, when estimating small force differences, where phase-sensitive OCT is particularly valuable, cvNNs outperform rvNNs with stacked inputs. Similar improvements can be achieved with separate processing in the dual-path models. The results emphasize the importance of model design in processing complex OCT signals and demonstrate the potential of cvNNs for phase-sensitive OCT. However, our work also highlights the current limitations of cvNNs, particularly computational cost and data dependency.Publicationtype: Conference Paper20 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Nodule detection and generation on chest X-rays: NODE21 challenge(IEEE, 2024-03-26); ; ; ; Pulmonary nodules may be an early manifestation of lung cancer, the leading cause of cancer-related deaths among both men and women. Numerous studies have established that deep learning methods can yield high-performance levels in the detection of lung nodules in chest X-rays. However, the lack of gold-standard public datasets slows down the progression of the research and prevents benchmarking of methods for this task. To address this, we organized a public research challenge, NODE21, aimed at the detection and generation of lung nodules in chest X-rays. While the detection track assesses state-of-the-art nodule detection systems, the generation track determines the utility of nodule generation algorithms to augment training data and hence improve the performance of the detection systems. This paper summarizes the results of the NODE21 challenge and performs extensive additional experiments to examine the impact of the synthetically generated nodule training images on the detection algorithm performance.Publicationtype: Journal Article22 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Convolutional transformer network for paranasal anomaly classification in the maxillary sinus(SPIE, 2024-02); ; ; ; Large-scale population studies have examined the detection of sinus opacities in cranial MRIs. Deep learning methods, specifically 3D convolutional neural networks (CNNs), have been used to classify these anomalies. However, CNNs have limitations in capturing long-range dependencies across the low and high level features, potentially reducing performance. To address this, we propose an end-to-end pipeline using a novel deep learning network called ConTra-Net. ConTra-Net combines the strengths of CNNs and self-attention mechanisms of transformers to classify paranasal anomalies in the maxillary sinuses. Our approach outperforms 3D CNNs and 3D Vision Transformer (ViT), with relative improvements in F1 score of 11.68% and 53.5%, respectively. Our pipeline with ConTra-Net could serve as an alternative to reduce misdiagnosis rates in classifying paranasal anomalies.Publicationtype: Conference Paper27 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Weakly supervised Medulloblastoma tumor classification using domain specific patch-level feature extractionMedulloblastoma (MB) is the most common embryonal tumour of the brain. In order to decide on an optimal therapy, laborious inspection of histopathological tissue slides by neuropathologists is necessary. Digital pathology with the support of deep learning methods can help to improve the clinical workflow. Due to the high resolution of histopathological images, previous work on MB classification involved manual selection of patches, making it a time consuming task. In order to leverage only slide labels for histopathology image classification, weakly supervised approaches first encode small patches into feature vectors using an ImageNet pretrained encoder based on convolutional neural networks. The representations of patches are further utilized to train a data-efficient attention-based learning method. Due to the domain shift between natural images and histopathology images, the encoder is not optimal for feature extraction for MB classification. In this study, we adapt weakly supervised learning for MB classification and examine different histopathological specific encoder architectures and weights for the MB classification task. The results show that ResNet encoders pretrained with histopathology images lead to better MB classification results compared to encoders pretrained on ImageNet. The best performing method uses a ResNet50 architecture, pretrained on histopathology images and achieves an area under the receiver operating curve (AUROC) value of 71.89%, improving the baseline model by 2%.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 , 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 , Unsupervised Anomaly Detection of Paranasal Anomalies in the Maxillary SinusDeep learning (DL) algorithms can be used to automate paranasal anomaly detection from Magnetic Resonance Imaging (MRI). However, previous works relied on supervised learning techniques to distinguish between normal and abnormal samples. This method limits the type of anomalies that can be classified as the anomalies need to be present in the training data. Further, many data points from normal and anomaly class are needed for the model to achieve satisfactory classification performance. However, experienced clinicians can segregate between normal samples (healthy maxillary sinus) and anomalous samples (anomalous maxillary sinus) after looking at a few normal samples. We mimic the clinicians ability by learning the distribution of healthy maxillary sinuses using a 3D convolutional auto-encoder (cAE) and its variant, a 3D variational autoencoder (VAE) architecture and evaluate cAE and VAE for this task. Concretely, we pose the paranasal anomaly detection as an unsupervised anomaly detection problem. Thereby, we are able to reduce the labelling effort of the clinicians as we only use healthy samples during training. Additionally, we can classify any type of anomaly that differs from the training distribution. We train our 3D cAE and VAE to learn a latent representation of healthy maxillary sinus volumes using L1 reconstruction loss. During inference, we use the reconstruction error to classify between normal and anomalous maxillary sinuses. We extract sub-volumes from larger head and neck MRIs and analyse the effect of different fields of view on the detection performance. Finally, we report which anomalies are easiest and hardest to classify using our approach. Our results demonstrate the feasibility of unsupervised detection of paranasal anomalies from MRIs with an AUPRC of 85% and 80% for cAE and VAE, respectively.Publicationtype: Conference Paper25