Sfoglia per Relatore  

Opzioni
Vai a: 0-9 A B C D E F G H I J K L M N O P Q R S T U V W X Y Z
Mostrati risultati da 1 a 20 di 121
Tipologia Anno Titolo Titolo inglese Autore File
Lauree triennali 2021 Addestramento di Reti Neurali attraverso l'algoritmo di ottimizzazione 'Discesa Stocastica del Gradiente con Momento' Neural Networks Training through the 'Stochastic Gradient Descent with Momentum' optimization algorithm RIGHETTO, JACOPO
Lauree triennali 2020 Addestramento di reti neurali convoluzionali mediante la rappresentazione spaziale delle proteine Convolutional neural networks trained using protein spatial representation ZENNARO, ANDREA
Lauree triennali 2021 Addestramento e distribuzione di un modello di machine learning Training and deployment of a machine learning model CAMPOSTRINI, GIANLUCA
Lauree magistrali 2024 AI Development on Rectal Cancer by using Medical Imaging. The growing incidence of rectal cancer has emphasized the need for advanced diagnostic and prognostic methodologies to enhance patient outcomes. This thesis shows the development of an artificial intelligence (AI) system that is designed to analyze rectal cancer through medical imaging. The process begins with using of 3D Slicer software for the precise identification and segmentation of tumor regions in medical images, including MRI and CT scans. This segmentation provides detailed anatomical information necessary for subsequent analytical stages. Following segmentation, a deep learning model is provided to predict various tumor characteristics such as stage, aggressiveness, and potential therapeutic response. The deep learning framework is based on convolutional neural networks (CNNs), selected for their superior performance in image analysis tasks. The model is trained and validated using a comprehensive dataset of annotated medical images, ensuring its robustness and generalizability across different patient populations. The synergy between 3D Slicer for accurate tumor identification and deep learning for predictive analytics aims to improve diagnostic precision and provide insightful prognostic information. This AI-driven approach aspires to assist clinicians in devising informed treatment strategies, thereby contributing to the advancement of personalized medicine in rectal cancer care. This research aims to demonstrate the potential of integrating advanced medical imaging techniques with AI to achieve more precise and efficient cancer management. The findings of this thesis offer a significant contribution to the field of oncology, particularly in rectal cancer, and highlight the importance of interdisciplinary collaboration in fostering medical innovation. FARMAN, AMIR MOHAMMAD
Lauree triennali 2022 Algoritmo per la generazione di immagini da dati tabellati Algorithm for generating images from tabular data LUCON XICCATO, GREGORY
Lauree magistrali 2023 An Empirical study of object detection methods with deep ensemble and stochastic selection of activation functions An Empirical study of object detection methods with deep ensemble and stochastic selection of activation functions ISMAIL, MUHAMMAD AQIB
Lauree triennali 2021 An empirical study on ensemble of segmentation approaches An empirical study on ensemble of segmentation approaches FORMAGGIO, ALBERTO
Lauree magistrali 2022 An Empirical Study on Segmentation Methods with Deep Ensembles and Data Augmentation An Empirical Study on Segmentation Methods with Deep Ensembles and Data Augmentation CUZA, DANIELA
Lauree triennali 2020 Analisi e collaudo di una applicazione Android in ambito bancario Analysis and testing of an Android application in the banking sector GUERRA, MATTEO
Lauree triennali 2020 Analisi EEG: rilevazione artefatti tramite Temporal Convolutional Network EEG analysis: artifacts detection through Temporal Convolutional Network MARTIN, MARCO
Lauree magistrali 2023 Anomaly Detection in Image Data using Denoising Diffusion Probabilistic Models Anomaly Detection in Image Data using Denoising Diffusion Probabilistic Models AZAD, FATEMEH
Lauree triennali 2020 Applicazione di reti neurali profonde a dataset sbilanciati per la classificazione di lesioni cutanee Application of deep learning on imbalanced datasets for skin lesion classification BINOTTO, STEFANO
Lauree magistrali 2023 Augmentation and Ensembles: Improving Medical Image Segmentation with SAM and Deep Networks Augmentation and Ensembles: Improving Medical Image Segmentation with SAM and Deep Networks CARISI, LORENZO
Lauree triennali 2022 Automazione di processi aziendali con P.A.D. Automation of company processes with P.A.D. ALUNNI, NICOLÒ
Lauree magistrali 2023 Autonomous Driving on Mars: Dataset and Models for Martian Terrain Segmentation Autonomous Driving on Mars: Dataset and Models for Martian Terrain Segmentation COCCO, ALESSIO
Lauree magistrali 2023 Autonomous Driving on Mars: From Dataset to Models - A Deep Learning Application on Martian Imagery Autonomous Driving on Mars: From Dataset to Models - A Deep Learning Application on Martian Imagery SALVIATI, UMBERTO
Lauree triennali 2022 Bioacoustic classification in the age of deep learning: A survey of methods and applications Bioacoustic classification in the age of deep learning: A survey of methods and applications NICHIFOR, ANTONELA
Lauree triennali 2021 Classificazione automatica di specie ittiche nell'Adriatico mediante l'utilizzo di tecniche di Deep Learning Automatic classification of fish species in the Adriatic Sea using Deep Learning techniques FANTIN, DAVIDE
Lauree triennali 2021 Classificazione della rappresentazione spaziale di enzimi mediante reti neurali convoluzionali 3D Enzyme classification using 3D convolutional neural networks on spatial representation VALENTINUZZI, ANDREA
Lauree triennali 2020 Classificazione di suoni ambientali mediante tecniche di Data augmentation e preprocessing dell'input Classification of environmental sounds by means of Data Augmentation techniques and preprocessing of the input POZZER, MATTEO
Mostrati risultati da 1 a 20 di 121
Legenda icone

  •  file ad accesso aperto
  •  file ad accesso riservato
  •  file sotto embargo
  •  nessun file disponibile