Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Gleason-grading of prostate cancer pathology specimens reveal the malignancy of the cancer tissues, thus provides critical guidance for prostate cancer diagnoses and treatment. Computer-aided automatic grading methods have been providing efficient and result-consistent alternative to traditional manually slide reading approach, through statistical and structural feature analysis of the digitized pathology slides. ,  propose a novel automatic Gleason grading algorithm through local structure model learning and classification.  use attributed graph to represent the tissue glandular structures in histopathology images; representative sub-graphs features re learned as bags-of-words features from labeled samples of each grades. Then structural similarity beten sub-graphs in the unlabeled images and the representative sub-graphs re obtained using the learned codebook. Gleason grade was given based on an overall similarity score.  validated the proposed algorithm on 300 prostate histopathology images from the TCGA dataset, and the algorithm achieved average grading accuracy of 91.25%, 76.36% and 64.75% on images with Gleason grade 3, 4 and 5 respectively.
['Daihou Wang', 'David J. Foran', 'Jian Ren', 'Hua Zhong', 'Isaac Yi Kim', 'Xin Qi']
I am writing a research paper on a idea Exploring automatic prostate histopathology image gleason grading via local structure modeling
[{"from": "human", "value": "I am writing a research paper on a idea Exploring automatic prostate histopathology image gleason grading via local structure modeling"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Gleason-grading of prostate cancer pathology specimens reveal the malignancy of the cancer tissues, thus provides critical guidance for prostate cancer diagnoses and treatment. Computer-aided automatic grading methods have been providing efficient and result-consistent alternative to traditional manually slide reading approach, through statistical and structural feature analysis of the digitized pathology slides. ,  propose a novel automatic Gleason grading algorithm through local structure model learning and classification.  use attributed graph to represent the tissue glandular structures in histopathology images; representative sub-graphs features re learned as bags-of-words features from labeled samples of each grades. Then structural similarity beten sub-graphs in the unlabeled images and the representative sub-graphs re obtained using the learned codebook. Gleason grade was given based on an overall similarity score.  validated the proposed algorithm on 300 prostate histopathology images from the TCGA dataset, and the algorithm achieved average grading accuracy of 91.25%, 76.36% and 64.75% on images with Gleason grade 3, 4 and 5 respectively."}]