Sökning: "segmenteringsmodell"

Visar resultat 1 - 5 av 10 uppsatser innehållade ordet segmenteringsmodell.

1. 1. Aggregating predictions of a yeast semantic segmentation model : Reducing a pixel classifier into a binary image classifier

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

   Författare :Ali Muquri; [2023]
   Nyckelord :;

   Sammanfattning : The introduction of machine learning in clinical microbiology is important for aiding clinical laboratories with highly repetitive tasks that are fatiguing, error-prone, and require long employee training time due to the complex nature of the task. A challenging task that belongs to the subareas that need assistance is yeast detection in fluorescence microscopy where various yeast morphologies exist. LÄS MER

3. 2. Deep Learning for Prostate Cancer Risk Prediction Through Image Analysis of Cells

   Master-uppsats, KTH/Skolan för kemi, bioteknologi och hälsa (CBH)

   Författare :Aditya Tejaswi; [2022]
   Nyckelord :Prostate Cancer; Multiple Instance Learning; Attention Scores; Cell Segmentation; Prostatacancer; Multiple Instance Learning; Uppmärksamhetspoäng; Cellsegmentering;

   Sammanfattning : Prostate cancer is one of the most common types of cancer occurring in men. Several types of research have been done using deep learning methods for the classification/prediction of cancer grades. In this thesis, the results of prostate cancer risk prediction, based only on the images of cells from the prostate tissues, have been analyzed. LÄS MER

4. 3. Learning from Synthetic Data : Towards Effective Domain Adaptation Techniques for Semantic Segmentation of Urban Scenes

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

   Författare :Gerard Valls I Ferrer; [2021]
   Nyckelord :Semantic Segmentation; Synthetic Data; Autonomous Driving; Domain Shift; Domain Adaptation; Domain Generalisation; Semantisk Segmentering; Syntetiska Data; Autonom Körning; Domänskift; Domänanpassning; Domängeneralisering;

   Sammanfattning : Semantic segmentation is the task of predicting predefined class labels for each pixel in a given image. It is essential in autonomous driving, but also challenging because training accurate models requires large and diverse datasets, which are difficult to collect due to the high cost of annotating images at pixel-level. LÄS MER

5. 4. Pushing the boundary of Semantic Image Segmentation

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

   Författare :Shipra Jain; [2020]
   Nyckelord :Deep Learning; computer vision; semantic segmentation; metric learning; contrastive learning; Djup lärning; datorsyn; semantisk segmentering; metrisk inlärning; kontrastivt lärande;

   Sammanfattning : The state-of-the-art object detection and image classification methods can perform impressively on more than 9k classes. In contrast, the number of classes in semantic segmentation datasets are fairly limited. This is not surprising , when the restrictions caused by the lack of labeled data and high computation demand are considered. LÄS MER

7. 5. Breast Cancer Risk Localization in Mammography Images using Deep Learning

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

   Författare :Beata Rystedt; [2020]
   Nyckelord :Deep learning; breast cancer risk; mammograms; convolutional networks; localization.; Djupinlärning; bröstcancerrisk; mammogram; faltningsnätverk; lokalisering.;

   Sammanfattning : Breast cancer is the most common form of cancer among women, with around 9000 new diagnoses in Sweden yearly. Detecting and localizing risk of breast cancer could give the opportunity for individualized examination programs and preventative measures if necessary, and potentially be lifesaving. LÄS MER