Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  present a new approach to model and classify breast parenchymal tissue. Given a mammogram, first,  will discover the distribution of the different tissue densities in an unsupervised manner, and second,  will use this tissue distribution to perform the classification.  achieve this using a classifier based on local descriptors and probabilistic Latent Semantic Analysis (pLSA), a generative model from the statistical text literature.  studied the influence of different descriptors like texture and SIFT features at the classification stage showing that textons outperform SIFT in all cases. Moreover  demonstrate that pLSA automatically extracts meaningful latent aspects generating a compact tissue representation based on their densities, useful for discriminating on mammogram classification.  show the results of tissue classification over the MIAS and DDSM datasets.  compare our method with approaches that classified these same datasets showing a better performance of our proposal.
['Anna Bosch', 'Xavier Muñoz', 'Arnau Oliver', 'Joan Martí']
I am writing a research paper on a idea Modeling and Classifying Breast Tissue Density in Mammograms
[{"from": "human", "value": "I am writing a research paper on a idea Modeling and Classifying Breast Tissue Density in Mammograms"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  present a new approach to model and classify breast parenchymal tissue. Given a mammogram, first,  will discover the distribution of the different tissue densities in an unsupervised manner, and second,  will use this tissue distribution to perform the classification.  achieve this using a classifier based on local descriptors and probabilistic Latent Semantic Analysis (pLSA), a generative model from the statistical text literature.  studied the influence of different descriptors like texture and SIFT features at the classification stage showing that textons outperform SIFT in all cases. Moreover  demonstrate that pLSA automatically extracts meaningful latent aspects generating a compact tissue representation based on their densities, useful for discriminating on mammogram classification.  show the results of tissue classification over the MIAS and DDSM datasets.  compare our method with approaches that classified these same datasets showing a better performance of our proposal."}]