Augmenting Datasets using Diffusion Models: Leveraging Machine Learning for Liquid Biopsy and cfDNA Analysis

Cancer is a leading cause of death worldwide. Early detection is crucial for improving the quality of life and increasing chances of survival. However, current cancer screening methods can be invasive and costly, deterring high-risk individuals.

Our project seeks to address this challenge by harnessing the power of machine learning and non-invasive liquid biopsy techniques.

By analyzing tumor-derived molecules in blood samples, we aim to develop a tool using cutting-edge generative models to augment existing datasets. This innovative approach will enhance detection accuracy from the limited amount of already available datasets.