Breast and lung cancers are the most common occurring cancers and have a marked impact on patient’s physical and mental health, which seriously threatens their lives and health.
The correct diagnosis, especially the early detection and treatment of breast and lung cancers, has a decisive impact on the prognosis.
We hypothesize that AI-based CAD systems can improve breast and lung cancer imaging, detection, classification, and diagnosis. We evaluate different AI/ML-enabled algorithms, including supervised, semi-supervised, and active learning methods on real-world data regarding the effect and evaluation of computer software in the detection, diagnosis, and image acquisition of breast and lung cancer diseases (e.g., CADe, CADx, Computer-aided image acquisition) in radiology, laying the groundwork for the evaluation of autonomous AI/ML-enabled devices.
Furthermore, the project will help us improve our understanding and discriminate the cases and non-cases of lung and breast cancer efficiently from imaging data and help identify the main variables needed to make these predictions.
