A Breast Cancer Visualization

In a world where early detection of breast cancer can significantly increase survival rates, it's alarming to learn that up to 30% of breast cancers may go undetected in routine screenings. In countries like Sweden, where women are recommended to undergo screening bi-annually, the potential to miss 30% of cancers is a strong reminder of the work that is yet to be done. This statistic is not just a number, it represents mothers, daughters, sisters, and friends who face unnecessary risks due to the limitations of current diagnostic methods. We are driven by the desire to protect the women in our lives—our moms, aunts, grandmothers—and women worldwide.

Our goal is to blend AI and human knowledge in the detection of breast cancer through a visualization tool aimed at radiologists. The ultimate objective is to let fewer cases slip through the cracks, and to offer every woman the best possible chance of a healthy future.

Data

The mammograms displayed in this visualization were obtained from the EA1411 dataset [2]. Patient names were randomly generated from a list of common swedish names. Patient ids were randomly generated. Heatmaps and scores were generated using neural networks trained specifically for this visualization similar to the ones described in [1].

Tools

Crafted with Svelte and D3.js for insightful visualizations, styled by DaisyUI and Tailwind.css for elegance, and brought online with Vercel for reliability. Figma aids our design precision.

Learning Goals

• Leveraging the principles of visual perception and data visualization techniques.
• Exploring different data visualization libraries and tools.
• Advancing our skillset in D3.js, Svelte and focussing on seamless web integration to enhance User Experience.
• Improving our understanding of how to create valuable visualizations by ensuring intuitive interpretation.
• Incorporating User Centred Design process by incorporating user feedback to iteratively refine our visualizations.
• Collaborating with domain matter experts to ensure the accuracy and relevance of our visualizations.

Acknowledgements

This project was developed as part of the course DH2321 VT24 Information Visualization offered by KTH Royal Institute of Technology. We would like to thank our course instructor Dr. Mario Romero Vega, as well as Alessandro Iop and Marko Petrovic for their guidance and support throughout the project.

References

• Liu Yue. 2024. Breast cancer risk assessment and detection in mammograms with artificial intelligence. DIVA. Retrieved from https://www.diva-portal.org/smash/record.jsf?pid=diva2%3A1818672
• Comstock, C. E., Gatsonis, C., Newstead, G. M., Snyder, B. S., Gareen, I. F., Bergin, J. T., Rahbar, H., Sung, J. S., Jacobs, C., Harvey, J. A., Nicholson, M. H., Ward, R. C., Holt, J., Prather, A., Miller, K. D., Schnall, M. D., & Kuhl, C. K. (2023). Abbreviated Breast MRI and Digital Tomosynthesis Mammography in Screening Women With Dense Breasts (EA1141) (Version 1) [dataset]. The Cancer Imaging Archive. https://doi.org/10.7937/2BAS-HR33
• Leibig C, Brehmer M, Bunk S, Byng D, Pinker K, Umutlu L. Combining the strengths of radiologists and AI for breast cancer screening: a retrospective analysis. Lancet Digit Health. 2022 Jul;4(7):e507-e519. PMID: 35750400; PMCID: PMC9839981. https://doi.org/10.1016/S2589-7500(22)00070-X
• Hongyan Gu, Jingbin Huang, Lauren Hung, and Xiang 'Anthony' Chen. 2021. Lessons Learned from Designing an AI-Enabled Diagnosis Tool for Pathologists. Proc. ACM Hum.-Comput. Interact. 5, CSCW1, Article 10 (April 2021), 25 pages. https://doi.org/10.1145/3449084
• Bo Gao and YiMing FuCha. 2019. Information Visualization in the Delivery of Postoperative Treatment Services for Breast Cancer. In Proceedings of the 12th International Symposium on Visual Information Communication and Interaction (VINCI '19). Association for Computing Machinery, New York, NY, USA, Article 30, 1–2. https://doi.org/10.1145/3356422.3356452
• Catalina Hallett. 2008. Multi-modal presentation of medical histories. In Proceedings of the 13th international conference on Intelligent user interfaces (IUI '08). Association for Computing Machinery, New York, NY, USA, 80–89. https://doi.org/10.1145/1378773.1378785