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A Distance Function based Cascaded Neural Network for accurate Polyps Segmentation and Classification

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In clinical practice, it is often difficult to locate and measure the size of polyps accurately for the follow-up surgical operation decision. In this paper, based on the position constraint between the primary organ and polyps boundary, we propose a U-Net based cascaded neural network for the joint segmentation of the organ of interest and polyps. The constraint on their position relation is further imposed by adding a narrow-band distance function and complimentary dice function to the loss function. Through a series of comparisons and ablation study, the proposed method with the cascaded network architecture and the additional loss functions was validated on an in-house dataset for gallbladder polyps segmentation and classification. It has been demonstrated that the proposed method achieved a significant improvement over conventional U-Net, U-Net++ etc.. Eventually, the pathological type classification based on the segmented polys shows 30% higher accuracy compared to those conventional ResNet based results. Biography Yuanhong Jiang is a Ph.D student from Institute of Natural Sciences, Shanghai Jiao Tong University. His research interest lies broadly in the area of medical image processing, deep learning and graph neural networks. His recent works include medical image segmentation and MR image reconstruction with deep learning approaches, and he is researching on image processing task with graph neural networks.

This talk is part of the Artificial Intelligence Research Group Talks (Computer Laboratory) series.

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