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Optimization of a Deep Neural Network Label Encoding in a Multi-Label Problem.
Zaťko, Martin ; Novotná, Petra (referee) ; Hejč, Jakub (advisor)
The aim of the diploma thesis is to propose a method of deep learning for the classification of arrhythmias from ECG recordings and to compare the effect of coding its outputs on the overall quality of the model. A 1D convolutional neural network was selected and methods of label coding using one-hot coding, ordinal coding, the method using an autoencoder and the word embbeding method were tested and compared on it. The obtained results show that the use of the word embbeding method can increase the classification capacity of the proposed network.
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Segmentation of bone lesions in spinal CT data
Zaťko, Martin ; Chmelík, Jiří (referee) ; Jakubíček, Roman (advisor)
The aim of the bachelor thesis was to get acquainted with the anatomy and oncological diseases of spine. Search for segmentation techniques and implement my chosen machine learning technique for the task of segmenting bone lesions of vertebral bodies. The U-net architecture of convolutional neural networks, which is generally widely used in the segmentation of biomedical images, was selected and implemented. The results obtained are high enough for the network to be used for initial rough detection and segmentation, but its use in the clinical world is not recommended.
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Optimization of a Deep Neural Network Label Encoding in a Multi-Label Problem.
Zaťko, Martin ; Novotná, Petra (referee) ; Hejč, Jakub (advisor)
The aim of the diploma thesis is to propose a method of deep learning for the classification of arrhythmias from ECG recordings and to compare the effect of coding its outputs on the overall quality of the model. A 1D convolutional neural network was selected and methods of label coding using one-hot coding, ordinal coding, the method using an autoencoder and the word embbeding method were tested and compared on it. The obtained results show that the use of the word embbeding method can increase the classification capacity of the proposed network.
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Segmentation of bone lesions in spinal CT data
Zaťko, Martin ; Chmelík, Jiří (referee) ; Jakubíček, Roman (advisor)
The aim of the bachelor thesis was to get acquainted with the anatomy and oncological diseases of spine. Search for segmentation techniques and implement my chosen machine learning technique for the task of segmenting bone lesions of vertebral bodies. The U-net architecture of convolutional neural networks, which is generally widely used in the segmentation of biomedical images, was selected and implemented. The results obtained are high enough for the network to be used for initial rough detection and segmentation, but its use in the clinical world is not recommended.
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