Abstract
This article designs a cascaded neural network to diagnose colonoscopic images automatically. With the limited number of labeled polyps masked in binary, the proposed detection network uses a hetero-encoder to map a colonoscopic image to an aggregated set of exemplified images as data argumentation to force the successive autoencoder to learn important features acting as a denoising autoencoder. In other words, the autoencoder denoises the transient images generated in the precedent hetero-encoder training process by auto-associating the ground truth and its variants. A hard attention model classifies the segmented image and applies a local region proposal network (RPN) to the generation and aggression of bounding boxes only on the segmented images to allow a more precise detection such that computations on bounding boxes with less information are avoided. The proposed system can outperform current complex state-of-art methods like faster-R-CNN from the experiments on endoscopic images.
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Funding
This study was funded by the National Key R&D Program of China (2017YFC0908200), the Key Technology Research and Development Program of Zhejiang Province (no. 2017C03017), and the Key Project of Yiwu Science and Technology plan, China. no. 20-3-067.
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Hwang, M., Qian, Y., Wu, C. et al. A local region proposals approach to instance segmentation for intestinal polyp detection. Int. J. Mach. Learn. & Cyber. 14, 1591–1603 (2023). https://doi.org/10.1007/s13042-022-01714-4
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DOI: https://doi.org/10.1007/s13042-022-01714-4