National Repository of Grey Literature 109 records found  1 - 10nextend  jump to record: Search took 0.00 seconds. 
Calculation of Bone Mineral Density from Dual-energy CT and its Application on Patient with Multiple Myeloma
Nohel, Michal ; Chmelík, Jiří
This article presents the results of the calculation of bone mineral density in the spine of a patient with multiple myeloma and lytic lesions. The findings indicate that the average value for a healthy vertebra falls within the physiological range. In the case of the patient with myeloma, a low value was measured in the area of the lytic lesion, suggesting a high risk of pathological fractures. The research also revealed lower values in areas without lytic lesions. These results emphasize the importance of precise evaluation of mineral density in the diagnosis of spinal diseases.
Potential of neural networks using capsules for medical image processing
Šipula, Samuel ; Vičar, Tomáš (referee) ; Chmelík, Jiří (advisor)
The following master thesis introduces the reader to a relatively new deep learning approach, the capsule neural network. The thesis describes the working principle of capsule networks and compares them with established convolutional networks. Further, the reader is introduced to the use of this technique in medical image processing. The practical part of the paper describes the procedure of learning a capsule network and a reference convolutional network on two datasets. The aim of the thesis is to compare the effect of dataset size on the resulting efficiency of the two types of networks.
Methods for initializing neural network weights and their effect on network learning
Prukner, Jakub ; Nemčeková, Petra (referee) ; Chmelík, Jiří (advisor)
This thesis examines the use of various methods for initialising the weights of artificial neural networks and monitoring their impact on network learning. Image classification from two databases, MNIST and CIFAR-10, is selected as the task for the network. The theoretical section provides an overview of the field of artificial neural networks, along with an analysis of different methods for initialising weights. The practical section includes a description of the experiments conducted, an explanation of the architectures and their associated hyperparameters. The individual experiments observe the effect of the selected methods and their respective configurations on the learning of different artificial neural network architectures. The results are compared for each dataset and architecture type, and the methods with which a the network achieved the best learning are selected. Furthermore, the methods with which the optimal learning of the network was achieved the fastest are selected. The results obtained are discussed.
Potential of neural networks using transformers for medical image processing
Valík, Tomáš ; Nohel, Michal (referee) ; Chmelík, Jiří (advisor)
This thesis explores the potential of neural networks based on transformer architecture for medical image processing. The main objective was to compare the performance of ResNet18 and Vision Transformer (ViT-B-16) models on two distinct datasets, specifically Intel Image Classification and ChestXray. The models were optimized using the Optuna framework and subsequently trained ten times each to ensure robustness of the results. These results indicate that models utilizing Vision Transformers achieve higher weighted F1 scores compared to ResNet18 models. Specifically, the ViT-B-16 model achieved the highest F1 score of 0.939 on the Intel Image dataset and 0.907 on the ChestXray dataset, whereas ResNet18 achieved scores of 0.883 and 0.885, respectively. Statistical analyses using the Wilcoxon test confirmed that the differences in performance between the models are statistically significant, suggesting an advantage of using Vision Transformers for these tasks. An analysis of computational complexity is also provided, highlighting that ViT requires significantly higher computational resources.
Implementation of thrombi detector in multiphase CT data
Rudol, Filip ; Chmelík, Jiří (referee) ; Jakubíček, Roman (advisor)
Early diagnosis is a key factor for the successful treatment of ischemic stroke. Software for automatic thrombi detection can be a helpful tool for radiologists. The bachelor’s thesis deals with thrombi detection in multiphase CT data. The main objective of this thesis was to detect thrombi and develop a prototype of the detection software. The algorithm also allows the thrombi to be segmented. The proposed approach is fully automatic. The success of the algorithm could be objectively evaluated using the available manual annotations. The practical part was implemented in the MATLAB R2023b programming environment. In the theoretical part, a research on the issue was developed.
Implementation of a deep learning model for spinal tumor segmentation of multiple myeloma patients in CT data
Gálík, Pavel ; Chmelík, Jiří (referee) ; Nohel, Michal (advisor)
Tato diplomová práce se zabývá implementací modelu hlubokého učení pro segmentaci páteřních nádorů pacientů s mnohočetným myelomem v CT datech. Práce seznamuje čtenáře s anatomií páteře, tématem mnohočetného myelomu a principy CT zobrazování. Hluboké učení se stává důležitou součástí vývoje počítačem podporovaných systémů detekce a diagnostiky, práce uvádí různé modely hlubokého učení pro segmentaci obrazu a pro segmentaci nádorů páteře byl implementován model nnU-Net.
Intracranial hemorrhage localization in axial slices of head CT images
Kopečný, Kryštof ; Chmelík, Jiří (referee) ; Nemček, Jakub (advisor)
This thesis is focused on detection of intracranial hemorrhage in CT images using both one-stage and two-stage object detectors based on convolutional neural networks. The fundamentals of intracranial hemorrhage pathology and CT imaging as well as essential insight into computer vision and object detection are listed in this work. The knowledge of these fields of studies is a starting point for the implemenation of hemorrhage detector. The use of open-source CT image datasets is also discussed. The final part of this thesis is a model evaluation on a test dataset and results examination.
Detection of foreign objects in X-ray chest images using machine learning methods
Matoušková, Barbora ; Kolář, Radim (referee) ; Chmelík, Jiří (advisor)
Foreign objects in Chest X-ray (CXR) cause complications during automatic image processing. To prevent errors caused by these foreign objects, it is necessary to automatically find them and ommit them in the analysis. These are mainly buttons, jewellery, implants, wires and tubes. At the same time, finding pacemakers and other placed devices can help with automatic processing. The aim of this work was to design a method for the detection of foreign objects in CXR. For this task, Faster R-CNN method with a pre-trained ResNet50 network for feature extraction was chosen which was trained on 4 000 images and lately tested on 1 000 images from a publicly available database. After finding the optimal learning parameters, it was managed to train the network, which achieves 75% precision, 77% recall and 76% F1 score. However, a certain part of the error is formed by non-uniform annotations of objects in the data because not all annotated foreign objects are located in the lung area, as stated in the description.
Identification of vertebrae type in CT data by machine learning methods
Matoušková, Barbora ; Kolář, Radim (referee) ; Chmelík, Jiří (advisor)
Identification of vertebrae type by machine learning is an important task to facilitate the work of medical doctors. This task is embarrassed by many factors. First, a spinal CT imagining is usually performed on patiens with pathologies such as lesions, tumors, kyphosis, lordosis, scoliosis or patients with various implants that cause artifacts in the images. Furthermore, the neighboring vertebraes are very similar which also complicates this task. This paper deals with already segmented vertebrae classification into cervical, thoracic and lumbar groups. Support vector machines (SVM) and convolutional neural networks (CNN) AlexNet and VGG16 are used for classification. The results are compared in the conclusion.
Methods of Detection, Segmentation and Classification of Difficult to Define Bone Tumor Lesions in 3D CT Data
Chmelík, Jiří ; Flusser,, Jan (referee) ; Kozubek, Michal (referee) ; Jan, Jiří (advisor)
The aim of this work was the development of algorithms for detection segmentation and classification of difficult to define bone metastatic cancerous lesions from spinal CT image data. For this purpose, the patient database was created and annotated by medical experts. Successively, three methods were proposed and developed; the first of them is based on the reworking and combination of methods developed during the preceding project phase, the second method is a fast variant based on the fuzzy k-means cluster analysis, the third method uses modern machine learning algorithms, specifically deep learning of convolutional neural networks. Further, an approach that elaborates the results by a subsequent random forest based meta-analysis of detected lesion candidates was proposed. The achieved results were objectively evaluated and compared with results achieved by algorithms published by other authors. The evaluation was done by two objective methodologies, technical voxel-based and clinical object-based ones. The achieved results were subsequently evaluated and discussed.

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See also: similar author names
1 Chmelik, J.
8 Chmelík, Jakub
3 Chmelík, Jakub Evan
6 Chmelík, Jan
2 Chmelík, Josef
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