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• W4214827367 abstract "Nuclei instance segmentation and classification in histology images is a challenging task, which requires not only a precise semantic segmentation at pixel-level but also an accurate classification at instance-level. To tackle the problem, in this paper, we propose a Double-Tower Yolact network named DT-Yolact, which introduces a bottom-up path augmentation (BPA) module to pair with the feature pyramid network (FPN) module in Yolact, with the purpose of shortening the transmission path and fuse the lower-level and higher-level features extracted from nuclei. Moreover, a dilated convolution (DC) algorithm is employed in the convolutional neural network to expand the convolution receptive field and further improve the accuracy of prediction. The comparison results on PanNuke histology images have shown that: the mAP of the proposed DT-Yolact network is 3.56% and 1.33% higher than those of the state-of-art Mask R-CNN and the original Yolact networks. Therefore, it is promising to apply the proposed DT-Yolact network to the simultaneous nuclei instance segmentation and classification in histopathological medical images." @default.
• W4214827367 created "2022-03-05" @default.
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• W4214827367 date "2021-12-01" @default.
• W4214827367 modified "2023-09-29" @default.
• W4214827367 title "Nuclei Instance Segmentation and Classification in Histopathological Images using a DT-Yolact" @default.
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• W4214827367 doi "https://doi.org/10.1109/iucc-cit-dsci-smartcns55181.2021.00072" @default.
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