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Generation of high affinity ICAM-1-specific nanobodies and evaluation of their suitability for allergy treatment

Ines Zettl, Tatiana Ivanova, Mohammed Zghaebi, Marina V. Rutovskaya, Isabella Ellinger, Oksana Goryainova, Jessica Kollárová, Sergio Villazala-Merino, Christian Lupinek, Christina Weichwald, Anja Drescher, Julia Eckl-Dorna,...[more]

 

Nothobranchius furzeri, the Turquoise Killifish: A Model of Age-Related Osteoporosis?

Maria Butylina, PhD-student in the group of Prof. Dr. Peter Pietschmann, published recently her research on Nothobranchius furzeri, the turquoise killifish, in Gerontology. [more]

 

Isolation of nanobodies with potential to reduce patients' IgE binding to the major birch pollen allergen, Bet v 1

Ines Zettl, Tatiana Ivanova, Maria R. Strobl, Christina Weichwald, Oksana Goryainova, Evgenia Khan, Marina V. Rutovskaya, Margarete Focke- Tejkl, Anja Drescher, Barbara Bohle, Sabine Flicker, Sergei V. Tillib Allergy 2022...[more]

 

Impaired Mineral Ion Metabolism in a Mouse Model of Targeted Calcium-Sensing Receptor (CaSR) Deletion from Vascular Smooth Muscle Cells

Martin Schepelmann (group Enikö Kallay) and national and international colleagues and collaborators have just published a study in the Journal of the American Society of Nephrology (JASN), one of the highest ranked and most...[more]

 

Vaccine based on folded RBD-PreS fusion protein with potential to induce sterilizing immunity to SARS-CoV-2 variants

The preclinical data for a vaccine developed at MedUni Vienna to protect against SARS-CoV-2 indicates that it is effective against all SARS-CoV-2 variants known to date, including omicron - even in those who have not yet built up...[more]

 

Birch pollen allergic patients have IgE and IgG antibodies binding to diverse patterns of conformational epitopes on the major allergen, Bet v 1

Schmalz S, Mayr V, Shosherova A, Gepp B, Ackerbauer D, Sturm G, Bohle B, Breiteneder H, Radauer C. Isotype-specific binding patterns of serum antibodies to multiple conformational epitopes of Bet v 1.[more]

 

Neutralization of SARS-CoV-2 requires antibodies against conformational receptor-binding domain epitopes.

Gattinger P, Niespodziana K, Stiasny K, Sahanic S, Tulaeva I, Borochova K, Dorofeeva Y, Schlederer T, Sonnweber T, Hofer G, Kiss R, Kratzer B, Trapin D, Tauber PA, Rottal A, Körmöczi U, Feichter M, Weber M, Focke-Tejkl M,...[more]

 
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Inhaltsbereich

Breast Cancer Histological Image Classification Using Fine-Tuned Deep Network Fusion

Amirreza Mahbod, Isabella Ellinger, Rupert Ecker, Örjan Smedby, and Chunliang Wang Springer International Publishing AG, part of Springer Nature 2018 A. Campilho et al. (Eds.): ICIAR 2018, LNCS 10882, pp. 754–762, 2018. doi.org/10.1007/978-3-319-93000-8_85

Histopathological image analysis, which is used for recognition and diagnosis of tissue abnormalities such as malignant lesions, is commonly performed by experienced pathologists. Computer-aided diagnosis (CAD) systems are semi or fully automated image analysis algorithms, which are developed to help pathologists during the diagnosis procedure. Being a second opinion system, CAD systems are supposed to reduce the workload of specialists, to improve the diagnosis efficiency, to increase the level of inter-observer agreement and, in the end, also contribute to cost reduction.

While there are a large number of automatic or semi-automatic methods for image analysis, machine learning-based algorithms have shown to be superior over conventional image processing techniques when multiple pattern types have to be recognized and distinguished. Convolutional Neural Networks (CNNs) are machine learning approaches for image analysis that do not rely on hand-crafted features, but utilize large amount of images to derive task-specific image features. While extremely promising, the research community still has to develop – and validate – appropriate algorithms.

Amirreza Mahbod is PhD student in an EU-funded European Training Network (ETN; Grant #675228, coordinated by Enikö Kallay (IPA); https://casr.meduniwien.ac.at) and co-supervised by Isabella Ellinger (IPA) and Rupert Ecker (CEO TissueGnostics GmbH). Amirreza makes use of deep CNNs for segmentation and classification of histopathological images with minimum pre- and post-processing steps. He utilizes state-of-the-art deep learning-based architectures and adapts them for histopathological image analysis. The algorithms are tested and validated in „Grand Challenges in biomedical image analysis“ such as the BACH (ICIAR 2018 Grand Challenge on BreAst Cancer Histology Images). The goal of the BACH challenge was to automatically classify H&E stained breast histology microscopy images in four classes: normal, benign, in situ carcinoma and invasive carcinoma. Amirreza´s method, which is described in the presented conference paper, was ranked place 18 of 51 participants. The performance of the method was evaluated based on the overall prediction accuracy. The ultimate goal is to integrate successfull classification and segmentation methods into the StrataQuestTM software of TissueGnostics GmbH and make them available for clinical as well as biomedical research applications.

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