A sketch for the issues with a view on the Bildungsroman

Der germanistische Ansatz einer Funktionsgeschichte literarisch- sozialer Institutionen ist im Zuge der transnationalen Wende der Literaturwissenschaften zu revidieren. Das Problem wird am Beispiel der Gattung »Bildungsroman « aufgezeigt, deren Entstehung vielfach als eine nationale Sonderentwicklung angesehen wird. Dagegen mehren sich neuerdings Hinweise auf die starke transnationale Verbreitung einer offenbar doch reisenden Form. Wie ließe der Prozess einer national und kulturell grenzüberschreitenden Bewegung sich funktionsgeschichtlich beschreiben?A functional-historical approach that considers literary genres as social institutions has been developed and fruitfully applied in German Studies. The current transnational turn in Literary Studies requires this approach to be revised. The Bildungsroman genre is a distinguished example, as it is widely held to be a national particularity. Recently, though the genre’s transnational spread has been pointed out. Obviously, we are dealing with a traveling form. How can we describe its movement across national and cultural boundaries, using a revised functional-historical approach

Automated segmentation of tissue images for computerized IHC analysis

This paper presents two automated methods for the segmentation ofimmunohistochemical tissue images that overcome the limitations of themanual approach aswell as of the existing computerized techniques. The first independent method, based on unsupervised color clustering, recognizes automatically the target cancerous areas in the specimen and disregards the stroma; the second method, based on colors separation and morphological processing, exploits automated segmentation of the nuclear membranes of the cancerous cells. Extensive experimental results on real tissue images demonstrate the accuracy of our techniques compared to manual segmentations; additional experiments show that our techniques are more effective in immunohistochemical images than popular approaches based on supervised learning or active contours. The proposed procedure can be exploited for any applications that require tissues and cells exploration and to perform reliable and standardized measures of the activity of specific proteins involved in multi-factorial genetic pathologie

Automated Identification of Thoracic Pathology from Chest Radiographs with Enhanced Training Pipeline

Chest x-rays are the most common radiology studies for diagnosing lung and heart disease. Hence, a system for automated pre-reporting of pathologic findings on chest x-rays would greatly enhance radiologists' productivity. To this end, we investigate a deep-learning framework with novel training schemes for classification of different thoracic pathology labels from chest x-rays. We use the currently largest publicly available annotated dataset ChestX-ray14 of 112,120 chest radiographs of 30,805 patients. Each image was annotated with either a 'NoFinding' class, or one or more of 14 thoracic pathology labels. Subjects can have multiple pathologies, resulting in a multi-class, multi-label problem. We encoded labels as binary vectors using k-hot encoding. We study the ResNet34 architecture, pre-trained on ImageNet, where two key modifications were incorporated into the training framework: (1) Stochastic gradient descent with momentum and with restarts using cosine annealing, (2) Variable image sizes for fine-tuning to prevent overfitting. Additionally, we use a heuristic algorithm to select a good learning rate. Learning with restarts was used to avoid local minima. Area Under receiver operating characteristics Curve (AUC) was used to quantitatively evaluate diagnostic quality. Our results are comparable to, or outperform the best results of current state-of-the-art methods with AUCs as follows: Atelectasis:0.81, Cardiomegaly:0.91, Consolidation:0.81, Edema:0.92, Effusion:0.89, Emphysema: 0.92, Fibrosis:0.81, Hernia:0.84, Infiltration:0.73, Mass:0.85, Nodule:0.76, Pleural Thickening:0.81, Pneumonia:0.77, Pneumothorax:0.89 and NoFinding:0.79. Our results suggest that, in addition to using sophisticated network architectures, a good learning rate, scheduler and a robust optimizer can boost performance.Comment: 6 pages, 1 figure, 2 table

Tensor based multichannel reconstruction for breast tumours identification from DCE-MRIs

A new methodology based on tensor algebra that uses a higher order singular value decomposition to perform three-dimensional voxel reconstruction from a series of temporal images obtained using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is proposed. Principal component analysis (PCA) is used to robustly extract the spatial and temporal image features and simultaneously de-noise the datasets. Tumour segmentation on enhanced scaled (ES) images performed using a fuzzy C-means (FCM) cluster algorithm is compared with that achieved using the proposed tensorial framework. The proposed algorithm explores the correlations between spatial and temporal features in the tumours. The multi-channel reconstruction enables improved breast tumour identification through enhanced de-noising and improved intensity consistency. The reconstructed tumours have clear and continuous boundaries; furthermore the reconstruction shows better voxel clustering in tumour regions of interest. A more homogenous intensity distribution is also observed, enabling improved image contrast between tumours and background, especially in places where fatty tissue is imaged. The fidelity of reconstruction is further evaluated on the basis of five new qualitative metrics. Results confirm the superiority of the tensorial approach. The proposed reconstruction metrics should also find future applications in the assessment of other reconstruction algorithms

Datengetriebene Analyse dynamischer Magnet-Resonanz-Tomographie-Aufnahmen für die Brustkrebsdiagnostik

Twellmann T. Data-driven analysis of dynamic contrast-enhanced magnetic resonance imaging data in breast cancer diagnosis. Bielefeld (Germany): Bielefeld University; 2005.In the European Union, breast cancer is the most common type of cancer affecting women. If diagnosed in an early stage, breast cancer has an encouraging cure rate. Thus, early detection of breast cancer continues to be the key for an effective treatment. Recently, Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) has been identified as a valuable complementary technique for breast imaging. DCE-MRI has demonstrated to be highly sensitive for the detection of cancer, motivating the initiation of several ongoing studies evaluating the potential of DCE-MRI as a screening tool for young women or women at high risk by virtue of genetic predispositions. In DCE-MRI, a temporal sequence of 3D MRI images of the female breast is recorded, depicting the temporal course of the concentration of a contrast agent in breast tissue. The temporal dynamics of the concentration enable radiologists to infer valuable information not only for differentiating between healthy and pathologically affected tissue, but also for distinguishing innocuous benign disorders from life-threatening carcinoma. This new type of information is inherent in the multi-temporal image sequence, but does not become evident to the observer by means of the individual images. For detecting and characterising pathological disorders of tiny tissue regions, radiologists are required to simultaneously consider the entire data; a challenging task due to the multi-temporal nature and the huge amount of 3D image data. Hence, there is a substantial demand for Computer-Aided Diagnosis (CAD) systems for supporting radiologists in the time consuming diagnosis process. The aim of the work as presented in this thesis is to develop computational approaches for DCE-MRI data analysis in breast cancer diagnosis. Central component of the presented approaches will be techniques from the field of artificial neural networks (ANN) and machine learning. ANNs allow for analysing DCE-MRI data from a data-driven and initially model-free perspective differing from the model-based perspective predominant in clinical practice. A central concept of the data-driven approaches to DCE-MRI analysis is example-based learning: Training signals reflecting the temporal courses of contrast agent concentrations in single voxels are exposed to unsupervised ANNs which in turn autonomously reveal categories of similar signals by virtue of their statistical features. Supervised ANNs are able to derive knowledge for the distinction of predetermined classes of signals from a sequence of training examples which were assigned by e.g. a human expert to one of the considered classes. After adaptation, the trained predictor is able to generalise from the seen to unseen examples and can be applied for detecting signals of the corresponding classes in DCE-MRI sequences of new cases. The initial identification of tissue masses affected by pathological disorders is considered as a binary classification problem. Linear discriminant analysis as well as state-of-the-art support vector machines for kernel-based learning are applied for voxel-by-voxel classification of temporal kinetic signals or textural features. The outcome is visualised as a new grey value image, enabling radiologists to identify suspicious tissue masses by means of a single 3D image. Analysis of the tumour masses themselves is supported by pseudo-colour representation of the DCE-MRI data. A hierarchical architecture of an ANN as well as a multi-class support vector machine with dedicated post-processing of the output is trained to distinguish temporal kinetic signals of healthy, benign and malignant tissue. The visual presentation of the outcome as a 3D RGB image reveals the heterogeneity of tumour tissue and provides valuable information about the tumour architecture. For further examination of the relation between pseudo-colour and multi-temporal signals, the adaptive colour-scale technique is proposed for simultaneous presentation of signal and colour space. In the last application, efficient visualisation of DCE-MRI data is considered as a dimensionality reduction problem. Principal component analysis and the non-linear kernel principal component analysis are applied to reduce the dimension of the signal space of temporal kinetic signals. The depiction of the reduced signal data allows for displaying DCE-MRI sequences by a reduced number of images and reveals information which is inherent in the data but not perceivable in the original images. The applicability of the different modules is demonstrated by means of DCE-MRI sequences recorded during routine examinations at the City Centre Hospital of the University of Munich, Germany, as well as sequences recorded within the MARIBS breast cancer screening study by the Institute of Cancer Research, UK. The validity of the results is demonstrated by means of ROC analyses as well as detailed qualitative comparison with established model-based techniques

Data driven analysis of dynamic contrast-enhanced magnetic resonance imaging data in breast cancer diagnosis

In the European Union, breast cancer is the most common type of cancer affecting women. If diagnosed in an early stage, breast cancer has an encouraging cure rate. Thus, early detection of breast cancer continues to be the key for an effective treatment. Recently, Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) has been identified as a valuable complementary technique for breast imaging. DCE-MRI has demonstrated to be highly sensitive for the detection of cancer, motivating the initiation of several ongoing studies evaluating the potential of DCE-MRI as a screening tool for young women or women at high risk by virtue of genetic predispositions. In DCE-MRI, a temporal sequence of 3D MRI images of the female breast is recorded, depicting the temporal course of the concentration of a contrast agent in breast tissue. The temporal dynamics of the concentration enable radiologists to infer valuable information not only for differentiating between healthy and pathologically affected tissue, but also for distinguishing innocuous benign disorders from life-threatening carcinoma. This new type of information is inherent in the multi-temporal image sequence, but does not become evident to the observer by means of the individual images. For detecting and characterising pathological disorders of tiny tissue regions, radiologists are required to simultaneously consider the entire data; a challenging task due to the multi-temporal nature and the huge amount of 3D image data. Hence, there is a substantial demand for Computer-Aided Diagnosis (CAD) systems for supporting radiologists in the time consuming diagnosis process. The aim of the work as presented in this thesis is to develop computational approaches for DCE-MRI data analysis in breast cancer diagnosis. Central component of the presented approaches will be techniques from the field of artificial neural networks (ANN) and machine learning. ANNs allow for analysing DCE-MRI data from a data-driven and initially model-free perspective differing from the model-based perspective predominant in clinical practice. A central concept of the data-driven approaches to DCE-MRI analysis is example-based learning: Training signals reflecting the temporal courses of contrast agent concentrations in single voxels are exposed to unsupervised ANNs which in turn autonomously reveal categories of similar signals by virtue of their statistical features. Supervised ANNs are able to derive knowledge for the distinction of predetermined classes of signals from a sequence of training examples which were assigned by e.g. a human expert to one of the considered classes. After adaptation, the trained predictor is able to generalise from the seen to unseen examples and can be applied for detecting signals of the corresponding classes in DCE-MRI sequences of new cases. The initial identification of tissue masses affected by pathological disorders is considered as a binary classification problem. Linear discriminant analysis as well as state-of-the-art support vector machines for kernel-based learning are applied for voxel-by-voxel classification of temporal kinetic signals or textural features. The outcome is visualised as a new grey value image, enabling radiologists to identify suspicious tissue masses by means of a single 3D image. Analysis of the tumour masses themselves is supported by pseudo-colour representation of the DCE-MRI data. A hierarchical architecture of an ANN as well as a multi-class support vector machine with dedicated post-processing of the output is trained to distinguish temporal kinetic signals of healthy, benign and malignant tissue. The visual presentation of the outcome as a 3D RGB image reveals the heterogeneity of tumour tissue and provides valuable information about the tumour architecture. For further examination of the relation between pseudo-colour and multi-temporal signals, the adaptive colour-scale technique is proposed for simultaneous presentation of signal and colour space. In the last application, efficient visualisation of DCE-MRI data is considered as a dimensionality reduction problem. Principal component analysis and the non-linear kernel principal component analysis are applied to reduce the dimension of the signal space of temporal kinetic signals. The depiction of the reduced signal data allows for displaying DCE-MRI sequences by a reduced number of images and reveals information which is inherent in the data but not perceivable in the original images. The applicability of the different modules is demonstrated by means of DCE-MRI sequences recorded during routine examinations at the City Centre Hospital of the University of Munich, Germany, as well as sequences recorded within the MARIBS breast cancer screening study by the Institute of Cancer Research, UK. The validity of the results is demonstrated by means of ROC analyses as well as detailed qualitative comparison with established model-based techniques

Nationalliteratur als Weltliteratur : Zur Aufgabe der Philologien

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