Junaid Abdul Wahid

Cancer subtype classification plays a pivotal role in personalised medicine, requiring the integration of diverse data types. Traditional prompting methods in vision-language models fail to fully leverage multimodal data, particularly when working with minimal labelled data. To address these limitations, we propose a novel framework that introduces the CancerFusionPrompt, a specialised prompting method for integrating imaging and multi-omics data.