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- W2959707910 abstract "Colorectal polyps are an indicator of colorectal cancer (CRC). Classification of polyps during colonoscopy is still a challenge for which many medical experts have come up with visual models albeit with limited success. In this paper, a classification approach is proposed to differentiate between polyp malignancy, using features extracted from the Global Average Pooling (GAP) layer of a Convolutional Neural Network (CNNs). Two recent endoscopic modalities are used to improve the algorithm prediction: Blue Laser Imaging (BLI) and Linked Color Imaging (LCI). Furthermore, a new strategy of per-class data augmentation is adopted to tackle an unbalanced class distribution and to improve the decision of the classifiers. As a result, we increase the performance compared to state-of-the-art methods (0.97 vs 0.90 AUC). Our method for automatic polyp malignancy classification facilitates future advances towards patient safety and may avoid time-consuming and costly histopathological assessment." @default.
- W2959707910 created "2019-07-23" @default.
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- W2959707910 date "2019-04-01" @default.
- W2959707910 modified "2023-10-15" @default.
- W2959707910 title "Multi-Modal Classification of Polyp Malignancy using CNN Features with Balanced Class Augmentation" @default.
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- W2959707910 doi "https://doi.org/10.1109/isbi.2019.8759320" @default.
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