Visualizing 10,000+ Publications on Breast Cancer Diagnosis & Screening

Challenge

This project leverages Natural Language Processing (NLP) and AI-driven clustering to turn unstructured scientific literature into a visually explorable knowledge map. By processing thousands of medical publications related to breast cancer screening, the system uncovers hidden patterns and topic relationships.

Poljects’ Solution

In this interactive dashboard:

• Over 10,000 publications are automatically clustered by semantic similarity
• Significant variables like publication date, title length, and keywords are used to refine exploration
• Topics like “Deep Learning Classification”, “Radiology”, “Covid-19 Pandemic”, and “Racial Interventions” emerge organically
• Users can filter and drill down into clusters to examine publication-level metadata

Built using:

• AI/NLP models for topic modeling & clustering
• Dimensionality reduction techniques (e.g. UMAP, t-SNE)
• Interactive front-end using Plotly + Dash

Outcome

This tool enables researchers, policymakers, and clinical experts to stay ahead of innovation trends, discover knowledge gaps, and accelerate evidence-based decision-making.

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