A Point-Neighborhood Learning Framework for Nasal Endoscope Image Segmentation

• URL: http://arxiv.org/abs/2405.20044v2
• Date: Wed, 04 Dec 2024 07:35:37 GMT
• Title: A Point-Neighborhood Learning Framework for Nasal Endoscope Image Segmentation
• Authors: Pengyu Jie, Wanquan Liu, Chenqiang Gao, Yihui Wen, Rui He, Weiping Wen, Pengcheng Li, Jintao Zhang, Deyu Meng,
• Abstract summary: This paper proposes a simple yet efficient weakly semi-supervised method called the Point-Neighborhood Learning (PNL) framework.<n>In PNL, we propose a point-neighborhood supervision loss and a pseudo-label scoring mechanism to explicitly guide the model's training.<n>The proposed method significantly improves performance without increasing the parameters of the segmentation neural network.
• Score: 41.46663118326646
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Lesion segmentation on nasal endoscopic images is challenging due to its complex lesion features. Fully-supervised deep learning methods achieve promising performance with pixel-level annotations but impose a significant annotation burden on experts. Although weakly supervised or semi-supervised methods can reduce the labelling burden, their performance is still limited. Some weakly semi-supervised methods employ a novel annotation strategy that labels weak single-point annotations for the entire training set while providing pixel-level annotations for a small subset of the data. However, the relevant weakly semi-supervised methods only mine the limited information of the point itself, while ignoring its label property and surrounding reliable information. This paper proposes a simple yet efficient weakly semi-supervised method called the Point-Neighborhood Learning (PNL) framework. PNL incorporates the surrounding area of the point, referred to as the point-neighborhood, into the learning process. In PNL, we propose a point-neighborhood supervision loss and a pseudo-label scoring mechanism to explicitly guide the model's training. Meanwhile, we proposed a more reliable data augmentation scheme. The proposed method significantly improves performance without increasing the parameters of the segmentation neural network. Extensive experiments on the NPC-LES dataset demonstrate that PNL outperforms existing methods by a significant margin. Additional validation on colonoscopic polyp segmentation datasets confirms the generalizability of the proposed PNL.

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