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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: with files , Data-efficient vision transformers for multi-label sisease classification on chest radiographs(De Gruyter, 2022-07); ; ; ; Radiographs are a versatile diagnostic tool for the detection and assessment of pathologies, for treatment planning or for navigation and localization purposes in clinical interventions. However, their interpretation and assessment by radiologists can be tedious and error-prone. Thus, a wide variety of deep learning methods have been proposed to support radiologists interpreting radiographs. Mostly, these approaches rely on convolutional neural networks (CNN) to extract features from images. Especially for the multi-label classification of pathologies on chest radiographs (Chest X-Rays, CXR), CNNs have proven to be well suited. On the Contrary, Vision Transformers (ViTs) have not been applied to this task despite their high classification performance on generic images and interpretable local saliency maps which could add value to clinical interventions. ViTs do not rely on convolutions but on patch-based self-attention and in contrast to CNNs, no prior knowledge of local connectivity is present. While this leads to increased capacity, ViTs typically require an excessive amount of training data which represents a hurdle in the medical domain as high costs are associated with collecting large medical data sets. In this work, we systematically compare the classification performance of ViTs and CNNs for different data set sizes and evaluate more data-efficient ViT variants (DeiT). Our results show that while the performance between ViTs and CNNs is on par with a small benefit for ViTs, DeiTs outperform the former if a reasonably large data set is available for training.Publicationtype: Journal ArticleScopus© Citations 3 28 51