Medical image analysis at the Biomedical Imaging Division at KTH
Abstract
Description:
This project collects 2 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. 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.
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.
Continuation projects:
This is a continuation of Berzelius-2025-212.
Relationship with KAW strategic initiatives:
The 2 PhD students from this project are affiliated with WASP and are part of the WASP graduate school.
