Sökning: "3D training"
Visar resultat 1 - 5 av 105 uppsatser innehållade orden 3D training.
1. Adoption of Additive Manufacturing in the Medical Industry within Sweden : Stakeholder analysis in the process of adoption of AM in the medical industry and their influence on each otherMaster-uppsats, Uppsala universitet/Institutionen för samhällsbyggnad och industriell teknik
Sammanfattning : Additive manufacturing (AM) is a printing technology which can produce 3-dimensional solid object by adding layers of material from 3D model data. AM has numerous benefits and can bring a new industrial revolution. To have a smooth transition in the technology, organizations must consider involved stakeholders’ interests. LÄS MER
- Master-uppsats, Lunds universitet/Sjukhusfysikerutbildningen
Sammanfattning : Introduction: Renography is a standard diagnostic examination that evaluates renal function, renal pelvic dilatation and urinary obstruction. Renography is performed by injecting a radiopharmaceutical (predominately 99mTc-MAG3) and using gamma camera to image the biodistribution in a dynamic sequence. LÄS MER
- Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)
Sammanfattning : The thesis proposes an unsupervised representation learning method to predict 3D human pose from a 2D skeleton via a VAEGAN (Variational Autoencoder Generative Adversarial Network) hybrid network. The method learns to lift poses from 2D to 3D using selfsupervision and adversarial learning techniques. LÄS MER
- Uppsats för yrkesexamina på avancerad nivå, Uppsala universitet/Avdelningen för visuell information och interaktion
Sammanfattning : Modern machine learning methods, utilising neural networks, require a lot of training data. Data gathering and preparation has thus become a major bottleneck in the machine learning pipeline and researchers often use large public datasets to conduct their research (such as the ImageNet  or MNIST  datasets). LÄS MER
- Master-uppsats, Uppsala universitet/Institutionen för farmaceutisk biovetenskap
Sammanfattning : Purpose This project aims to explore the classification method of kinase inhibitors with five-channel cell painting image data based on the deep learning model. Methods A ResNet50 transfer learning model was used as the starting point to build the deep neural network (DNN) model, where different DNN parameters were selected to make the deep learning model more suitable for the cell painting data. LÄS MER