A Bayesian hidden Potts mixture model for analyzing lung cancer pathology images

Business & Economics

• Mixture Model 100%
• Lung Cancer 90%
• Tumor 65%
• Pathology 50%
• Spatial Pattern 31%
• Cancer 26%
• Interaction 24%
• Progression 22%
• Lymphocytes 19%
• Metropolis-Hastings Algorithm 19%
• Bayesian Hierarchical Model 18%
• Random Field 17%
• Lung 16%
• Image Analysis 16%
• Deep Learning 15%
• Spatial Interaction 14%
• Learning Methods 14%
• Markov Chain Monte Carlo 13%
• Imaging 13%
• Modeling 13%
• Sampling 11%
• Screening 11%
• Characterization 8%
• Methodology 5%

Mathematics

• Lung Cancer 78%
• Potts Model 72%
• Mixture Model 60%
• Cell 42%
• Tumor 27%
• Interaction 22%
• Spatial Pattern 17%
• Progression 14%
• Cancer 13%
• Digital Imaging 11%
• Lymphocytes 9%
• Bayesian Hierarchical Model 9%
• Prognosis 8%
• Normalizing Constant 8%
• Lung 8%
• Metropolis-Hastings Algorithm 8%
• Modeling 8%
• Markov Random Field 8%
• Monte Carlo Sampling 7%
• Image Analysis 7%
• High Resolution 7%
• Square Lattice 7%
• Screening 7%
• Markov Chain Monte Carlo 6%
• Quantify 6%
• Model 6%
• Irregular 6%
• Predict 5%
• Learning 5%
• Methodology 4%
• Characterization 3%

Medicine & Life Sciences

• Lung Neoplasms 45%
• Pathology 36%
• Stromal Cells 16%
• Neoplasms 15%
• Markov Chains 11%
• Deep Learning 11%
• Cell Communication 7%
• Lymphocytes 5%
• Lung 4%