Pathomic Fusion: An Integrated Framework for Fusing Histopathology and Genomic Features for Cancer Diagnosis and Prognosis

Cancer diagnosis, prognosis, mymargin and therapeutic response predictions are based on morphological information from histology slides and molecular profiles from genomic data. However, most deep learning-based objective outcome prediction and grading paradigms are based on histology or genomics al...

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Bibliographic Details
Main Authors:	Chen, R.J (Author), Lindeman, N.I (Author), Lu, M.Y (Author), Mahmood, F. (Author), Rodig, S.J (Author), Wang, J. (Author), Williamson, D.F.K (Author)
Format:	Article
Language:	English
Published:	Institute of Electrical and Electronics Engineers Inc. 2022
Subjects:	Cancer diagnosis Convolutional networks Deep learning Diagnosis Diseases Forecasting Genes Genomic data genomics Genomics glioma Glioma Grading Graph convolutional network graph convolutional networks Ground truth Histological Techniques histology Histology human Humans Image fusion Multi-modal fusion Multimodal learning Multi-modal learning Outcome prediction pathology procedures prognosis Prognosis RNA survival analysis Survival analysis Unimodal
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View Fulltext in Publisher

Pathomic Fusion: An Integrated Framework for Fusing Histopathology and Genomic Features for Cancer Diagnosis and Prognosis

Internet

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