Papers › NeoUNet: Towards accurate colon polyp segmentation and neoplasm detection

NeoUNet: Towards accurate colon polyp segmentation and neoplasm detection

11 Jul 2021arXiv:2107.05023archive 2025-07-28

Phan Ngoc Lan, Nguyen Sy An, Dao Viet Hang, Dao Van Long, Tran Quang Trung, Nguyen Thi Thuy, Dinh Viet Sang

Automatic polyp segmentation has proven to be immensely helpful for endoscopy procedures, reducing the missing rate of adenoma detection for endoscopists while increasing efficiency. However, classifying a polyp as being neoplasm or not and segmenting it at the pixel level is still a challenging task for doctors to perform in a limited time. In this work, we propose a fine-grained formulation for the polyp segmentation problem. Our formulation aims to not only segment polyp regions, but also identify those at high risk of malignancy with high accuracy. In addition, we present a UNet-based neural network architecture called NeoUNet, along with a hybrid loss function to solve this problem. Experiments show highly competitive results for NeoUNet on our benchmark dataset compared to existing polyp segmentation models.

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