A bayesian mark interaction model for analysis of tumor pathology images

Mathematics

• Tumor 74%
• Spatial Correlation 72%
• Cancer 54%
• Cell 51%
• Interaction 50%
• Progression 40%
• Imaging 38%
• Digital Imaging 30%
• Spatial Point Pattern 27%
• MCMC Algorithm 25%
• Prognosis 24%
• Lung Cancer 24%
• Normalizing Constant 23%
• Spatial Pattern 23%
• Digital Image 23%
• Model 23%
• Multitype 22%
• High Resolution 21%
• Auxiliary Variables 19%
• Conditioning 18%
• Posterior distribution 17%
• Benchmark 17%
• Predict 16%
• Learning 15%
• Model-based 15%
• Classify 13%
• Methodology 12%
• Modeling 11%
• Demonstrate 10%
• Framework 10%

Business & Economics

• Tumor 100%
• Spatial Correlation 71%
• Imaging 63%
• Cancer 62%
• Pathology 61%
• Interaction 38%
• Progression 34%
• Lung Cancer 27%
• Deep Learning 24%
• Spatial Pattern 23%
• Posterior Distribution 22%
• Learning Methods 21%
• Markov Chain Monte Carlo 21%
• Digital Technology 19%
• Conditioning 18%
• Inference 14%
• Benchmark 13%
• Modeling 10%
• Methodology 8%

Engineering & Materials Science

• Pathology 79%
• Tumors 71%
• Imaging techniques 40%
• Deep learning 15%
• Tissue 14%
• Data acquisition 14%