Description: This project collects 6 different subprojects from the Division of Biomedical Imaging at KTH that require the use of GPUs for AI. This is a brief description of them: 1. Deep learning-based breast image analysis (PhD student Zhikai Yang). Goal: A) use AI for the detection, segmentation, and classification of breast cancer images. B) Image synthesis and translation of breast cancer images. C) Perform image reconstruction of CT and DBT with primal-dual neural networks. D) Generate reports from mammograms. 2. AI-based Indolent Lymphoma Classification (Research Engineer Simone Bendazzoli). Goal: use ViT models to predict, from an initial estimate, the 3D bounding boxes of the Lymphoma lesions in PET-CT data. 3. Generative models for diffusion MRI (PhD student Sanna Persson). Goal: use generative models to estimate diffusion MRI data with a given b-value (a scanning parameter) from images acquired with a different b-value. These images will be used to predict the mechanical properties of brain tissue. 4. Machine learning methods for brain magnetic resonance elastography (MRE) (PhD student Hampus Möller). Goal: combine physics-informed neural networks, neural operators and system identification to estimate the mechanical properties of the tissues from raw MRE data. 5. Transformer-based Spatio-temporal PET Image Reconstruction (PhD student Nicolas De Bie). Goal: apply transformer-based networks to improve both spatial and temporal aspects of PET image reconstruction from a large dataset of PET sinogram images. 6. Precision Medicine for Enhanced Personalized Treatments of Dementia Diseases Using AI-Powered Longitudinal MRI Retrieval (Postdoc Félix Nieto del Amor): Goal: develop a Content-Based Image Retrieval (CBIR) system using a large database of longitudinal brain Magnetic Resonance Imaging (MRI) of patients with dementia. All subprojects use anonymized data without sensitive information or synthetic data. Impact: All sub-projects aim to improve the quality of the images, increase accuracy, or add extra information that can potentially be used by clinicians in the future, which will potentially benefit the healthcare system and society in general.