Mass Detection

Mass detection research focuses on accurately identifying masses, particularly in medical imaging (e.g., mammography) and nuclear physics. Current efforts leverage deep learning models, including convolutional neural networks and transformer-based architectures, often incorporating techniques like weakly supervised learning and generative adversarial networks (GANs) to address data limitations and improve robustness. These advancements aim to enhance diagnostic accuracy in healthcare and improve the predictive power of nuclear mass models, impacting areas such as cancer detection and stellar nucleosynthesis. The field is actively exploring methods to mitigate biases and improve the generalizability of models across diverse datasets and imaging conditions.