TY - GEN
T1 - Understanding Tumor Micro Environment Using Graph Theory
AU - Rohail, Kinza
AU - Bashir, Saba
AU - Ali, Hazrat
AU - Alam, Tanvir
AU - Khan, Sheheryar
AU - Wu, Jia
AU - Chen, Pingjun
AU - Qureshi, Rizwan
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - Based over the historical data statistics of about past 50 years from National Cancer Institute’s Surveillance, the survival rate of patients affected with Chronic Lymphocytic Leukemia (CLL) is about 65%. Neoplastic lymphomas accelerated Chronic Lymphocytic Leukemia (aCLL) and Richter Transformation - Diffuse Large B-cell Lymphoma (RT-DLBL) are the aggressive and rare variant of this cancer that are subjected to less survival rate in patients and becomes worse with age of the patients. In this study, we developed a framework based over Graph Theory, Gaussian Mixture Modeling and Fuzzy C-mean Clustering, for learning the cell characteristics in neoplastic lymphomas along with quantitative analysis of pathological facts observed with integration of Image and Nuclei level analysis. On H &E slides of 60 hematolymphoid neoplasms, we evaluated the proposed algorithm and compared it to four cell level graph-based algorithms, including the global cell graph, cluster cell graph, hierarchical graph modeling and FLocK. The proposed method achieves better performance than the existing algorithms with mean diagnosis accuracy of 0.70833.
AB - Based over the historical data statistics of about past 50 years from National Cancer Institute’s Surveillance, the survival rate of patients affected with Chronic Lymphocytic Leukemia (CLL) is about 65%. Neoplastic lymphomas accelerated Chronic Lymphocytic Leukemia (aCLL) and Richter Transformation - Diffuse Large B-cell Lymphoma (RT-DLBL) are the aggressive and rare variant of this cancer that are subjected to less survival rate in patients and becomes worse with age of the patients. In this study, we developed a framework based over Graph Theory, Gaussian Mixture Modeling and Fuzzy C-mean Clustering, for learning the cell characteristics in neoplastic lymphomas along with quantitative analysis of pathological facts observed with integration of Image and Nuclei level analysis. On H &E slides of 60 hematolymphoid neoplasms, we evaluated the proposed algorithm and compared it to four cell level graph-based algorithms, including the global cell graph, cluster cell graph, hierarchical graph modeling and FLocK. The proposed method achieves better performance than the existing algorithms with mean diagnosis accuracy of 0.70833.
KW - Digital pathology
KW - Fuzzy clustering
KW - Graph theory
KW - Hematolymphoid cancer
UR - https://www.scopus.com/pages/publications/85151060281
UR - https://www.mendeley.com/catalogue/c7a0a869-41f8-3ee0-9cbc-671a8d1efbc8/
U2 - 10.1007/978-3-031-27066-6_7
DO - 10.1007/978-3-031-27066-6_7
M3 - Conference contribution
AN - SCOPUS:85151060281
SN - 9783031270659
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 90
EP - 101
BT - Computer Vision – ACCV 2022 Workshops - 16th Asian Conference on Computer Vision, Revised Selected Papers
A2 - Zheng, Yinqiang
A2 - Keleş, Hacer Yalim
A2 - Koniusz, Piotr
PB - Springer Science and Business Media Deutschland GmbH
T2 - 16th Asian Conference on Computer Vision , ACCV 2022
Y2 - 4 December 2022 through 8 December 2022
ER -
