Essays about: "strålbehandling"
Showing result 16 - 20 of 82 essays containing the word strålbehandling.
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16. Deep Learning with Importance Sampling for Brain Tumor MR Segmentation
University essay from KTH/Optimeringslära och systemteoriAbstract : Segmentation of magnetic resonance images is an important part of planning radiotherapy treat-ments for patients with brain tumours but due to the number of images contained within a scan and the level of detail required, manual segmentation is a time consuming task. Convolutional neural networks have been proposed as tools for automated segmentation and shown promising results. READ MORE
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17. Reinforcement learning applied to MLC tracking
University essay from KTH/Medicinteknik och hälsosystemAbstract : Radiotherapy has become an ever more successful treatment option for cancer.Advances in imaging protocols combined with precise therapy devices suchas linear accelerators contribute towards millimeter precision of treatmentdelivery with far fewer side effects. READ MORE
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18. Production and Evaluation of a Bombesin Analogue Conjugated to the Albumin-Binding Domain and DOTA for Prostate Cancer Radiotherapy
University essay from KTH/ProteinvetenskapAbstract : Prostate cancer is one of the most common types of cancer worldwide and claims hundreds of thousands of lives annually. Currently the most common treatment for prostate cancer is external beam radiotherapy, however, this treatment comes with serious side effects since it lacks selectivity for the cancer cells. READ MORE
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19. Toxicity of Pulsed Beams in Radiation Therapy from a Physio-Chemical Perspective
University essay from Uppsala universitet/Institutionen för fysik och astronomiAbstract : A significant portion of cancer patients receive radiotherapy as part of their curative or palliative treatment plan. Radiotherapy is however greatly limited by radiation induced toxicities in healthy tissue surrounding the tumour, which can lead to long-term or acute complications for a patient. READ MORE
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20. Overcoming generative likelihood bias for voxel-based out-of-distribution detection
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Deep learning-based dose prediction is a promising approach to automated radiotherapy planning but carries with it the risk of failing silently when the inputs are highly abnormal compared to the training data. One way to address this issue is to develop a dedicated outlier detector capable of detecting anomalous patient geometries. READ MORE