Sökning: "convolutional neural network"

Visar resultat 6 - 10 av 762 uppsatser innehållade orden convolutional neural network.

1. 6. Exploring the Depth-Performance Trade-Off : Applying Torch Pruning to YOLOv8 Models for Semantic Segmentation Tasks

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Xinchen Wang; [2024]
   Nyckelord :Deep Learning; Semantic segmentation; Network optimization; Network pruning; Torch Pruning; YOLOv8; Network Depth; Djup lärning; Semantisk segmentering; Nätverksoptimering; Nätverksbeskärning; Fackelbeskärning; YOLOv8; Nätverksdjup;

   Sammanfattning : In order to comprehend the environments from different aspects, a large variety of computer vision methods are developed to detect objects, classify objects or even segment them semantically. Semantic segmentation is growing in significance due to its broad applications in fields such as robotics, environmental understanding for virtual or augmented reality, and autonomous driving. LÄS MER

3. 7. Virtual H&E Staining Using PLS Microscopy and Neural Networks

   Master-uppsats, Lunds universitet/Matematik LTH

   Författare :Sally Vizins; Hanna Råhnängen; [2024]
   Nyckelord :Deep learning; Virtual staining; Skin tissue; Hematoxylin Eosin; H E; Pathology; Carcinoma; Point light source illumination; Neural Networks; GANs; Generative adversarial networks; CNNs; Convolutional neural networks; Relativistic generative adversarial network; Unet; Digital microscopy; Attention-Unet; Dense-Unet; Mathematics and Statistics;

   Sammanfattning : Histopathological examination, crucial in diagnosing diseases such as cancer, traditionally relies on time- and resource-consuming, poorly standardized chemical staining for tissue visualization. This thesis presents a novel digital alternative using generative neural networks and a point light source (PLS) microscope to transform unstained skin tissue images into their stained counterparts. LÄS MER

4. 8. Classifying femur fractures using federated learning

   Master-uppsats, Linköpings universitet/Statistik och maskininlärning

   Författare :Hong Zhang; [2024]
   Nyckelord :Atypical femur fracture; Federated Learning; Neural Network; Classification;

   Sammanfattning : The rarity and subtle radiographic features of atypical femoral fractures (AFF) make it difficult to distinguish radiologically from normal femoral fractures (NFF). Compared with NFF, AFF has subtle radiological features and is associated with the long-term use of bisphosphonates for the treatment of osteoporosis. LÄS MER

5. 9. Leveraging CNN for Automated Peak Picking in Untargeted Metabolomics without Parameter Dependencies

   Master-uppsats, Göteborgs universitet/Institutionen för data- och informationsteknik

   Författare :Vivian Wang; Lidia Yalew; [2023-10-19]
   Nyckelord :Deep learning; Convolutional neural network; LC-MS; Peak Detection; Metabolomics; Faster R-CNN;

   Sammanfattning : Metabolomics is a scientific discipline that involves the thorough analysis of small molecules, known as metabolites, found within a biological system. Furthermore, liquid chromatography-mass spectrometry (LC-MS) is a commonly used analytical technique in metabolomics for analysing biological samples due to its broad coverage of the measurable metabolome. LÄS MER

7. 10. An Empirical Survey of Bandits in an Industrial Recommender System Setting

   Master-uppsats, Göteborgs universitet/Institutionen för data- och informationsteknik

   Författare :Tobias Schwarz; Johan Brandby; [2023-09-21]
   Nyckelord :computer science; industrial application; machine learning; reinforcement learning; multi-armed bandits; MAB; contextual multi-armed bandits; survey; batch learning;

   Sammanfattning : In this thesis, the effects of incorporating unstructured data—images in the wild—in contextual multi-armed bandits are investigated, when used within a recommender system setting, which focuses on picture-based content suggestion. The idea is to employ image features, extracted by a pre-trained convolutional neural network, and study the resulting bandit behaviors when including respective excluding this information in the typical context creation, which normally relies on structured data sources—such as metadata. LÄS MER