Liebe alle,
Simon Hoffmann verteidigt am 29.05. um 14 Uhr in
unserem Labor (WE5/05.013) seine Masterarbeit. Dazu wird herzlich eingeladen. Titel und Zusammenfassung
siehe unten.
Viele Grüße
Johannes Rabold
Comparing Non-Pretrained Deep Learning Architectures for Subcellular Protein Pattern Classification in Confocal Microscope Images
Simon Hoffmann
Accurately classifying of subcellular protein-patterns in microscope images remains a major challenge for experts and deep learning systems. The nature of cells – including cell division, variable states and fine-grained structures – poses an extraordinary recognition setting. Due to their excellent recognition capability, high-performance deep neural networks are predestined for an efficient and accurate analysis of these sophisticated medical images. Adequate networks could enhance the understanding of proteins and subcellular functions, which is crucial to defeat serious diseases such as cancer. However, network architectures that can cope with these complex pictorial features have been inadequately evaluated so far. Hence, this master’s thesis aims at evaluating appropriate state-of-the-art deep learning architectures in order to highlight their performance in subcellular protein pattern classification in images of the Cell Atlas dataset. For this purpose, the four deep learning architectures DenseNet, Inception-ResNet-V2, DFL-CNN and GapNet-PL are implemented. Furthermore, the classification performance and computational requirements of all architectures are compared against each other. For reasons of objectivity, all models are utilised without prior training. The results of the evaluation provide crucial insights into the suitability of the architectures for dealing with complex cell characteristics. Ultimately, this study shows that all investigated architectures demonstrate exceptional accomplishment – albeit divergent - in classifying fine-grained protein and subcellular pattern in confocal microscope images.