Multiple Instance Attention Learning for Multimodal Classification and Detection for Breast Cancer Diagnosis

Summary

This project focuses on improving the analysis of breast cancer images, which is important for detecting and treating breast cancer in women. We want to help radiologists by providing a computer system that can assist in two main tasks: classifying images and outlining the areas of concern. The first task involves giving a score to the image, while the second task requires marking the specific areas of potential cancer. To achieve this, we are developing a system that can learn from examples without needing detailed annotations. This means we can use more readily available image-level labels instead of requiring specific annotations for each lesion. By doing this, we hope to make the diagnosis process faster and more accurate, potentially saving lives by aiding in early detection of breast cancer. This research is important not only for medical professionals but also for the broader community as it can improve the effectiveness and efficiency of breast cancer screening and diagnosis. In the future, this approach may have broader applications in other areas of medical imaging and help researchers develop better diagnostic tools for various diseases.