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- W3100934346 abstract "Deep learning has recently become a key methodology for the study and interpretation of cancer histology images. The ability of convolutional neural networks (CNNs) to automatically learn features from raw data without the need for pathologist expert knowledge, as well as the availability of annotated histopathology datasets, have contributed to a growing interest in deep learning applications to histopathology. In clinical practice for cancer, histopathological images have been commonly used for diagnosis, prognosis, and treatment. Recently, molecular subtype classification has gained significant attention for predicting standard chemotherapy's outcomes and creating personalized targeted cancer therapy. Genomic profiles, especially gene expression data, are mostly used for molecular subtyping. In this study, we developed a novel, PanCancer CNN model based on Google Inception V3 transfer learning to classify molecular subtypes using histopathological images. We used 22,484 Haemotoxylin and Eosin (HE macro-average=0.90). In cancer studies, combining histopathological images with genomic data has rarely been explored. We investigated the relationship between features extracted from HE macro-average=0.97). These results show that integrating H&E images and gene expression profiles can enhance accuracy of molecular subtype classification." @default.
- W3100934346 created "2020-11-23" @default.
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- W3100934346 date "2020-09-21" @default.
- W3100934346 modified "2023-09-24" @default.
- W3100934346 title "Integrative Deep Learning for PanCancer Molecular Subtype Classification Using Histopathological Images and RNAseq Data" @default.
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- W3100934346 doi "https://doi.org/10.1145/3388440.3412414" @default.
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