Sökning: "Deep learning"
Visar resultat 1 - 5 av 1347 uppsatser innehållade orden Deep learning.
Sammanfattning : Purpose: The aim of this study was to introduce a volumetric convolutional neural network for segmentation of the kidneys in SPECT images and to apply it in the dosimetry of radiopharmaceuticals of this organ, in order to decrease segmentation time and to standardize the segmentation of the kidneys. Method: Three networks were trained using two network architectures and a total of 216 retrospectively collected images from patients that underwent imaging procedures at Sahlgrenska University Hospital between 2009 and 2018. LÄS MER
2. Comparing Weak and Strong Annotation Strategies for Multiple Instance Learning in Digital PathologyMaster-uppsats, KTH/Skolan för kemi, bioteknologi och hälsa (CBH)
Sammanfattning : Prostate cancer is the second most diagnosed cancer worldwide and its diagnosis is done through visual inspection of biopsy tissue by a pathologist, who assigns a score used by doctors to decide on the treatment. However, the scoring system, the Gleason score, is affected by a high inter and intra-observer variability, lack of standardization, and overestimation. LÄS MER
- Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)
Sammanfattning : This study examined the relative performance of Deep Reinforcement Learning compared to a neuroevolution algorithm called NEAT when used to train AIs in a discrete game environment. Today there are many AI techniques to choose from among which NEAT and RL have become popular alternatives. LÄS MER
- Master-uppsats, Lunds universitet/Matematik LTH
Sammanfattning : Blood tests are an important part of modern medicine, and are essentially always stained using chemical colorization methods before analysis by computational or manual methods. The staining process allows different parts of blood cells to be discerned that would be unnoticeable in unstained blood. LÄS MER
5. Deep neural networks for food waste analysis and classification : Subtraction-based methods for the case of data scarcityUppsats för yrkesexamina på avancerad nivå, Uppsala universitet/Signaler och system
Sammanfattning : Machine learning generally requires large amounts of data, however data is often limited. On the whole the amount of data needed grows with the complexity of the problem to be solved. Utilising transfer learning, data augmentation and problem reduction, acceptable performance can be achieved with limited data for a multitude of tasks. LÄS MER