Magnetic Resonance Imaging (MRI) is a vital non-invasive tool for high-resolution brain imaging, yet its long acquisition time frequently results in patient movement leading to motion artifacts, particularly among non-compliant patients like newborns. While De…
Tato diplomová práce se zabývá využitím metod hlubokého učení pro sémantickou segmentaci leteckých snímků za účelem mapování průjezdnosti terénu pro mobilní roboty. Práce analyzuje současný stav poznání v oblasti hlubokých neuronových sítí a souvisejících dato…
Automated mechanical inspections increasingly rely on deep learning models for semantic segmentations of faults. However, standard deterministic models, such as U-Net, are prone to overconfident misclassifications on Out of Distribution (OOD) Data, posing seve…
Кваліфікаційну роботу присвячено дослідженню та програмній імплементації згорткової нейронної мережі архітектури U-Net для автоматизованої семантичної сегментації просторових даних. У процесі дослідження формалізовано математичну постановку задачі піксельної к…
Medical image segmentation plays a critical role in clinical diagnosis and treatment planning; however, the performance of deep learning models typically depends on large quantities of pixel-wise annotated data, which are costly and time-consuming to obtain. T…
This research represents a hybrid model for polyp segmentation in medical images, which combines convolutional neural networks (CNN) and vision transformers (ViT). We propose a hybrid architecture that integrates UNet with a Vision Transformer, where UNet is u…
Computed tomography (CT) is widely used for the diagnosis and assessment of pulmonary and hepatic diseases because it provides detailed anatomical information.With the development of deep learning, CT image analysis has advanced significantly in segmentation,…
The way we experience the world is multimodal, each of our five senses perceives the world differently, yet the brain can explore this information at once with ease. This work explores the potential of mimicking this exceptional cognitive capability in machine…
Large-scale pretraining has produced powerful deep learning representations, but real-world data rarely match the clean, uniform assumptions of standard transfer learning. This dissertation uses heterogeneous data to describe settings in which information diff…
Autonomous driving has progressed rapidly in recent years, driven largely by advances in machine learning, computer vision, and probabilistic optimization methods. Despite this progress, the deployment of autonomous systems in complex, safety-critical real- wo…
Advances in deep learning have revolutionized scene understanding in recent years, achieving unprecedented performance levels. As a result, nearly all autonomous vehicle perception systems now rely on learning-based Deep Neural Networks (DNNs). These methods a…
Prostate cancer (PCa) remains among the most prevalent and deadly cancers among men worldwide, with rising incidence rates placing increasing pressure on healthcare systems. Magnetic Resonance Imaging (MRI), and more specifically, Diffusion-Weighted Imaging (D…
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