The ultimate goal of our research is to build safe and ethical computational tools for improving image-based detection and diagnosis of disease. We are actively developing new algorithms for object detection, image segmentation and classification with applications in diagnosis of cardiovascular disease, detection of breast cancer, radiotherapy planning, quantification of brain lesions, and many more.
Selected publications
Prospective implementation of AI-assisted screen reading to improve early detection of breast cancer, Nature Medicine, 2023
Development and Evaluation of Machine Learning in Whole-Body Magnetic Resonance Imaging for Detecting Metastases in Patients With Lung or Colon Cancer: A Diagnostic Test Accuracy Study, Investigative Radiology, 2023
Multi-vendor evaluation of artificial intelligence as an independent reader for double reading in breast cancer screening on 275,900 mammograms, BMC Cancer, 2023
Artificial Intelligence as Supporting Reader in Breast Screening: A Novel Workflow to Preserve Quality and Reduce Workload, Journal of Breast Imaging, 2023
Multiple Instance Learning with Auxiliary Task Weighting for Multiple Myeloma Classification, International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2021
Multi-class semantic segmentation and quantification of traumatic brain injury lesions on head CT using deep learning – an algorithm development and multi-centre validation study, The Lancet Digital Health, 2020
Improving RetinaNet for CT Lesion Detection with Dense Masks from Weak RECIST Labels, International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2019
