Improving The Robustness And Stability Of A Machine Learning Model For Breast Cancer Prognosis Through The Use Of Multi-modal Classifiers

This article discusses the use of biomedical information retrieval techniques to develop early prognosis and diagnosis systems for breast cancer patients. These systems provide oncologists with plenty of information from various modalities to make the correct and feasible treatment plan for breast cancer patients. The article also examines end-to-end systems with two main components: (a) dimensionality reduction and (b) machine learning algorithms. The article concludes by highlighting the importance of these systems in providing accurate prognosis and diagnosis for breast cancer patients.