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Wyniki wyszukiwania dla: u-net
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Urban scene semantic segmentation using the U-Net model
PublikacjaVision-based semantic segmentation of complex urban street scenes is a very important function during autonomous driving (AD), which will become an important technology in industrialized countries in the near future. Today, advanced driver assistance systems (ADAS) improve traffic safety thanks to the application of solutions that enable detecting objects, recognising road signs, segmenting the road, etc. The basis for these functionalities...
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Modified U-Net with attention gate for enhanced automated brain tumor segmentation
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Automatic music signal mixing system based on one-dimensional Wave-U-Net autoencoders
PublikacjaThe purpose of this paper is to show a music mixing system that is capable of automatically mixing separate raw recordings with good quality regardless of the music genre. This work recalls selected methods for automatic audio mixing first. Then, a novel deep model based on one-dimensional Wave-U-Net autoencoders is proposed for automatic music mixing. The model is trained on a custom-prepared database. Mixes created using the...
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Automatic audio signal mixing system based on one-dimensional Wave-U-Net autoencoders
PublikacjaThe purpose of this dissertation is to develop an automatic song mixing system that is capable of automatically mixing a song with good quality in any music genre. This work recalls first the audio signal processing methods used in audio mixing, and it describes selected methods for automatic audio mixing. Then, a novel architecture built based on one-dimensional Wave-U-Net autoencoders is proposed for automatic music mixing. Models...
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Optimizing Brain Tumor Segmentation Through CNN U-Net with CLAHE-HE Image Enhancement
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Smart Approach for Glioma Segmentation in Magnetic Resonance Imaging using Modified Convolutional Network Architecture (U-NET)
PublikacjaSegmentation of a brain tumor from magnetic resonance multimodal images is a challenging task in the field of medical imaging. The vast diversity in potential target regions, appearance and multifarious intensity threshold levels of various tumor types are few of the major factors that affect segmentation results. An accurate diagnosis and its treatment demand strict delineation of the tumor affected tissues. Herein, we focus on...
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Automatic Brain Tumor Segmentation Using Convolutional Neural Networks: U-Net Framework with PSO-Tuned Hyperparameters
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Improved Brain Tumor Segmentation Using Modified U-Net based on Particle Swarm Optimization Image Enhancement
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Wojciech Chrzanowski dr hab. inż.
OsobyWojciech Chrzanowski, urodzony w 1952 r. w Toruniu, ukończył w 1975 r. studia magisterskie na kierunku Technologia Chemiczna, w specjalności Technologia Nieorganiczna (specjalizacja: ochrona przed korozją), na Wydziale Chemicznym PG, uzyskując dyplom z wyróżnieniem. W tym samym roku został zatrudniony w Zakładzie Technik Analitycznych ówczesnego Instytutu Inżynierii Chemicznej i Technik Pomiarowych, początkowo na stanowisku technicznym,...
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Detection of the Oocyte Orientation for the ICSI Method Automation
PublikacjaAutomation or even computer assistance of the popular infertility treatment method: ICSI (Intracytoplasmic Sperm Injection) would speed up the whole process and improve the control of the results. This paper introduces a preliminary research for automatic spermatozoon injection into the oocyte cytoplasm. Here, the method for detection a correct orientation of the polar body of the oocyte is presented. Proposed method uses deep...
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Romanika Okraszewska dr inż. arch.
Osobydr inż. arch. Romanika Okraszewska jest adiunktem w Katedrze Inżynierii Drogowej i Transportowej Wydziału Inżynierii Lądowej i Środowiska Politechniki Gdańskiej. W 1996 ukończyła klasę matematyczno-informatyczną w VIII Liceum Ogólnokształcącym im. Komisji Edukacji Narodowej w Gdańsku. Absolwentka dwóch wydziałów Politechiki Gdańksiej, w roku 2002 ukończyła studia architektury i urbanistyki a w 2004 zarządzania i ekonomii. W latach...
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Deep Learning-Based Cellular Nuclei Segmentation Using Transformer Model
PublikacjaAccurate segmentation of cellular nuclei is imperative for various biological and medical applications, such as cancer diagnosis and drug discovery. Histopathology, a discipline employing microscopic examination of bodily tissues, serves as a cornerstone for cancer diagnosis. Nonetheless, the conventional histopathological diagnosis process is frequently marred by time constraints and potential inaccuracies. Consequently, there...
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Comparison of image pre-processing methods in liver segmentation task
PublikacjaAutomatic liver segmentation of Computed Tomography (CT) images is becoming increasingly important. Although there are many publications in this field there is little explanation why certain pre-processing methods were utilised. This paper presents a comparison of the commonly used approach of Hounsfield Units (HU) windowing, histogram equalisation, and a combination of these methods to try to ascertain what are the differences...
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Abdominal Aortic Aneurysm segmentation from contrast-enhanced computed tomography angiography using deep convolutional networks
PublikacjaOne of the most common imaging methods for diagnosing an abdominal aortic aneurysm, and an endoleak detection is computed tomography angiography. In this paper, we address the problem of aorta and thrombus semantic segmentation, what is a mandatory step to estimate aortic aneurysm diameter. Three end-to-end convolutional neural networks were trained and evaluated. Finally, we proposed an ensemble of deep neural networks with underlying...
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Deep learning based thermal image segmentation for laboratory animals tracking
PublikacjaAutomated systems for behaviour classification of laboratory animals are an attractive alternative to manual scoring. However, the proper animals separation and tracking, especially when they are in close contact, is the bottleneck of the behaviour analysis systems. In this paper, we propose a method for the segmentation of thermal images of laboratory rats that are in close contact during social behaviour tests. For this, we are...
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A Mammography Data Management Application for Federated Learning
PublikacjaThis study aimed to develop and assess an application designed to enhance the management of a local client database consisting of mammographic images with a focus on ensuring that images are suitably and uniformly prepared for federated learning applications. The application supports a comprehensive approach, starting with a versatile image-loading function that supports DICOM files from various medical imaging devices and settings....
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Segmentation-Based BI-RADS ensemble classification of breast tumours in ultrasound images
PublikacjaBackground: The development of computer-aided diagnosis systems in breast cancer imaging is exponential. Since 2016, 81 papers have described the automated segmentation of breast lesions in ultrasound images using arti- ficial intelligence. However, only two papers have dealt with complex BI-RADS classifications. Purpose: This study addresses the automatic classification of breast lesions into binary classes (benign vs. ma- lignant)...
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Aleksandra Giełdoń - Paszek dr hab.
OsobyDoktor habilitowany w dziedzinie nauk o sztuce, historyk sztuki. Studiowała historię sztuki na Wydziale Filozoficzno-Historycznym Uniwersytetu Jagiellońskiego w Krakowie. W roku 2002 na Wydziale Historycznym tejże uczelni uzyskała tytuł doktora nauk humanistycznych w zakresie nauk o sztuce na podstawie dysertacji: Malarstwo pejzażowe a szkolnictwo artystyczne w Polsce (do 1939 roku). W roku 2015 została doktorem habilitowanym w...