Identifying Breast Cancer Prognosis Using Adaptive Feature Modulation With Multi-Scale Dynamic Loss (Afm-Msdl) Approach

Breast cancer prediction is still a very difficult problem in oncology because the disease is so different and patient results are so different. Deep learning and traditional machine learning have shown promise, but they often have trouble generalising across different datasets and under-represented classes. To get around these problems, we suggest a new approach called Adaptive Feature Modulation with Multi-Scale Dynamic Loss (AFM-MSDL). It is meant to improve both the accuracy of classification and the prediction of survival in breast cancer diagnosis. The AFM part changes the features that are taken from different types of data, like imaging, genetic, and clinical data, in a way that is adaptable. This makes sure that important but situation-specific traits are emphasised. Cross-scale interaction processes help this dynamic feature ranking even more, so the model can pick up both fine-grained and high-level predictive trends. In addition to AFM, the MSDL module adds a customisable loss formulation that balances classification and survival goals on different scales in real time. MSDL improves gradient stability, stops overfitting, and speeds up convergence by changing the input of each loss term in real time. The framework also uses pre-trained language models or medical knowledge graphs to add relevant knowledge. This helps the system work better with rare cases and features that aren't used very often. We tested AFM-MSDL on large-scale standard datasets like TCGA and METABRIC, along with clinical datasets, after putting it through strong preparation steps like normalisation, enhancement, and data imputation for missing values.