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PVC detection in ECG
Imramovská, Klára ; Hejč, Jakub (referee) ; Ronzhina, Marina (advisor)
The thesis deals with problems of automatic detection of premature ventricular contractions in ECG records. One detection method which uses a convolutional neural network and LSTM units is implemented in the Python language. Cardiac cycles extracted from one-lead ECG were used for detection. F1 score for binary classification (PVC and normal beat) on the test dataset reached 96,41 % and 81,76 % for three-class classification (PVC, normal beat and other arrhythmias). Lastly, the accuracy of the classification is evaluated and discussed, the achieved results for binary classification are comparable to the results of methods described in different papers.
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Detection of specific anatomical structures in CT data via convolutional neural networks
Kozlová, Dominika ; Jan, Jiří (referee) ; Jakubíček, Roman (advisor)
This thesis deals with the issue of detection of anatomical structures in medical images using convolutional neural networks (CNN). At first there are described methods of machine learning, convolutional neural networks and selected methods for detection using CNN. In this work was created a database of annotated CT images of ten anatomical structures (head, heart, aorta, left and right lung, spine, liver, left and right kidney, spleen). A method for detecting these structures was designed, that contains two approaches of region proposals from image, CNN and postprocessing to obtain the detection result. The designed algorithm was implemented in the Python programming language using the TensorFlow library. Obtained results of validation of the network and the detection results are presented and discussed in the last chapter.
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Prediction of radiotherapy response in rectal cancer by MR
Chmela, Radek ; Nohel, Michal (referee) ; Mézl, Martin (advisor)
This diploma thesis deals with the issue of predicting the response of rectal cancer to radiotherapy. The work is divided into four chapters. In the first two, the anatomy of the rectum, types of cancer and individual diagnostic methods are described, together with algorithms for detecting objects in images. In the third chapter, there is a description of the solution for automatic segmentation and prediction of the effectiveness of radiotherapy. In the fourth chapter, the achieved results are discussed.
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Interpreting the learning process of an atrial fibrillation classifier
Lichtblauová, Anna ; Ředina, Richard (referee) ; Novotná, Petra (advisor)
In the theoretical part of the bachelor thesis the problems of atrial fibrillation (AF) detection and principles of convolutional neural networks (CNN) are discussed. Next, two classifiers were created in the practical part. The first was designed to classify sinus rhythm, atrial fibrillation and other pathologies, while the second further distinguished the category "atrial fibrillation" according to whether it was present in the whole recording or only in a part of it. The resulting accuracies are 82.12 \% and 85.14 \% for the first and second classifiers, respectively.
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Tram Detection in Video by Neural Network
Golda, Vojtěch ; Špaňhel, Jakub (referee) ; Dyk, Tomáš (advisor)
This paper deals with tram detection in video using convolutional neural networks. The basic principles of their function are described. A number of distinct architectures are trained. The usefulness of the resulting models is subsequently compared. The output of this paper is a program capable of detecting trams in video.
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Tram Detection in Video by Neural Network
Golda, Vojtěch ; Špaňhel, Jakub (referee) ; Dyk, Tomáš (advisor)
This paper deals with tram detection in video using convolutional neural networks. The basic principles of their function are described. A number of distinct architectures are trained. The usefulness of the resulting models is subsequently compared. The output of this paper is a program capable of detecting trams in video.
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DEEP LEARNING FOR SINGLE-VOXEL AND MULTIDIMENSIONAL MR-SPECTROSCOPIC SIGNAL QUANTIFICATION, AND ITS COMPARISON WITH NONLINEAR LEAST-SQUARES FITTING
Shamaei, Amir Mohammad ; Latta,, Peter (referee) ; Kozubek, Michal (referee) ; Jiřík, Radovan (advisor)
Pro získání koncentrace metabolitů ve vyšetřované tkáni ze signálů magnetické rezonanční spektroskopie (MRS) je nezbytné provézt předzpracování, analýzu a kvantifikaci MRS signálu. Rychlý, přesný a účinný proces zpracování (předzpracování, analýza a kvantifikace) MRS dat je však náročný. Tato práce představuje nové přístupy pro předzpracování, analýzu a kvantifikaci MRS dat založené na hlubokém učení (DL). Navržené metody potvrdily schopnost použití DL pro robustní předzpracování dat, rychlou a efektivní kvantifikaci MR spekter, odhad koncentrací metabolitů in vivo a odhad nejistoty kvantifikace. Navržené přístupy výrazně zlepšily rychlost předzpracování a kvantifikace MRS signálu a prokázaly možnost použití DL bez učitele. Z hlediska přesnosti byly získány výsledky srovnatelné s tradičními metodami. Dále byl zaveden standardní formát dat, který usnadňuje sdílení dat mezi výzkumnými skupinami pro aplikace umělé inteligence. Výsledky této studie naznačují, že navrhované přístupy založené na DL mají potenciál zlepšit přesnost a efektivitu zpracování MRS dat pro lékařskou diagnostiku. Disertační práce je rozdělena do čtyř částí: úvodu, přehledu současného stavu výzkumu, shrnutí cílů a úkolů a souboru publikací, které představují autorův přínos v oblasti aplikací DL v MRS.
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Dataset augmentation with style transfer methods
Wolny, Michał ; Ligocki, Adam (referee) ; Kratochvíla, Lukáš (advisor)
This bachelor's thesis focuses on the research of dataset augmentation and style transfer methods. From the range of available style transfer algorithms, three very different methods were selected, implemented and then experimentally used for dataset augmentation. The effectiveness of augmentation using these methods was verified by performing a statistical analysis of each newly created dataset compared to the original, unmodified dataset. The results of the analysis provide important information about changes in statistical characteristics such as entropy, mean, median, variance, and standard deviation. This information helped to evaluate the effectiveness and impact of the augmentation methods used on the augmented dataset and provide evidence of their potential.
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Application for Car Logo Recognition
Uchytil, Tomáš ; Götthans, Jakub (referee) ; Kadlec, Petr (advisor)
Práce se zabývá nalezením vhodné neuronové sítě pro rozpoznání loga automobilové značky a implementací a trénováním této sítě. Ta je následně implementována do mobilní aplikace, která umožňuje rozpoznání loga na základě nově pořízené fotografie, nebo na základě obrázku vybraného z úložiště mobilního telefonu.
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