The goal of this master thesis was to propose a suitable strategy to detect and classify objects of interest in mammogram images. A part of this goal was to implement an experimentation framework, that will be used for data preparation, model training and comparison. Patch and full-image versions of the dataset were used in the analysis. Initialisation with weights that were pretrained on the images from other domain improved classifier performance. ResNet-34 had better AUC scores on the test set that ResNet-18. Semi-supervised training using entropy minimisation has no significant improvement over the supervised one. The thesis includes the visualisation of the network predictions and the analysis of the knowledge representation of the classier. The achieved results for a patch version of the dataset are comparable to the results of another article that utilised the same test set. For a full-image dataset the results of the training were suboptimal. 1
| Identifer | oai:union.ndltd.org:nusl.cz/oai:invenio.nusl.cz:434886 |
| Date | January 2020 |
| Creators | Ihnatchenko, Luka |
| Contributors | Mrázová, Iveta, Pilát, Martin |
| Source Sets | Czech ETDs |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/masterThesis |
| Rights | info:eu-repo/semantics/restrictedAccess |
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