{"title":"用于息肉分割的新型非预处理深度监督网络","authors":"Zhenni Yu , Li Zhao , Tangfei Liao , Xiaoqin Zhang , Geng Chen , Guobao Xiao","doi":"10.1016/j.patcog.2024.110554","DOIUrl":null,"url":null,"abstract":"<div><p>In this paper, we propose a non-pretrained deep supervision network (NPD-Net) for polyp segmentation. Unlike previous deep supervision networks that rely on ground truth (GT) or pre-training with GT to supervise deep features(the prediction maps from decoder), we propose a novel deep supervision strategy that directly utilizes the GT encoder (that encodes GT to get its maps) after initialization to mitigate overfitting and enhance generalization ability without pre-training, in other words, a non-pretrained. This strategy makes up the gap of directly using GT for deep supervision while mitigates the risk of overfitting due to leverage the well-train pre-trained weights on a small polyp datasets. In addition, we introduce a simple and efficient parallel dual attention module (PDA) to enhance the global modeling ability. PDA executes spatial and channel attention in parallel, and adopts implicit positional encoding and transpose operation to reduce computational complexity. Finally, NPD-Net is able to effectively supervise deep features, expand the range of context information acquisition and improve segmentation performance, particularly in terms of generalization ability. Our experimental results on five benchmark datasets demonstrate that NPD-Net outperforms other state-of-the-art methods. The code will be available at <span>https://github.com/guobaoxiao/NPD-Net</span><svg><path></path></svg>.</p></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":null,"pages":null},"PeriodicalIF":7.5000,"publicationDate":"2024-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A novel non-pretrained deep supervision network for polyp segmentation\",\"authors\":\"Zhenni Yu , Li Zhao , Tangfei Liao , Xiaoqin Zhang , Geng Chen , Guobao Xiao\",\"doi\":\"10.1016/j.patcog.2024.110554\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>In this paper, we propose a non-pretrained deep supervision network (NPD-Net) for polyp segmentation. Unlike previous deep supervision networks that rely on ground truth (GT) or pre-training with GT to supervise deep features(the prediction maps from decoder), we propose a novel deep supervision strategy that directly utilizes the GT encoder (that encodes GT to get its maps) after initialization to mitigate overfitting and enhance generalization ability without pre-training, in other words, a non-pretrained. This strategy makes up the gap of directly using GT for deep supervision while mitigates the risk of overfitting due to leverage the well-train pre-trained weights on a small polyp datasets. In addition, we introduce a simple and efficient parallel dual attention module (PDA) to enhance the global modeling ability. PDA executes spatial and channel attention in parallel, and adopts implicit positional encoding and transpose operation to reduce computational complexity. Finally, NPD-Net is able to effectively supervise deep features, expand the range of context information acquisition and improve segmentation performance, particularly in terms of generalization ability. Our experimental results on five benchmark datasets demonstrate that NPD-Net outperforms other state-of-the-art methods. The code will be available at <span>https://github.com/guobaoxiao/NPD-Net</span><svg><path></path></svg>.</p></div>\",\"PeriodicalId\":49713,\"journal\":{\"name\":\"Pattern Recognition\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":7.5000,\"publicationDate\":\"2024-05-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Pattern Recognition\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0031320324003054\",\"RegionNum\":1,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Pattern Recognition","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0031320324003054","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
A novel non-pretrained deep supervision network for polyp segmentation
In this paper, we propose a non-pretrained deep supervision network (NPD-Net) for polyp segmentation. Unlike previous deep supervision networks that rely on ground truth (GT) or pre-training with GT to supervise deep features(the prediction maps from decoder), we propose a novel deep supervision strategy that directly utilizes the GT encoder (that encodes GT to get its maps) after initialization to mitigate overfitting and enhance generalization ability without pre-training, in other words, a non-pretrained. This strategy makes up the gap of directly using GT for deep supervision while mitigates the risk of overfitting due to leverage the well-train pre-trained weights on a small polyp datasets. In addition, we introduce a simple and efficient parallel dual attention module (PDA) to enhance the global modeling ability. PDA executes spatial and channel attention in parallel, and adopts implicit positional encoding and transpose operation to reduce computational complexity. Finally, NPD-Net is able to effectively supervise deep features, expand the range of context information acquisition and improve segmentation performance, particularly in terms of generalization ability. Our experimental results on five benchmark datasets demonstrate that NPD-Net outperforms other state-of-the-art methods. The code will be available at https://github.com/guobaoxiao/NPD-Net.
期刊介绍:
The field of Pattern Recognition is both mature and rapidly evolving, playing a crucial role in various related fields such as computer vision, image processing, text analysis, and neural networks. It closely intersects with machine learning and is being applied in emerging areas like biometrics, bioinformatics, multimedia data analysis, and data science. The journal Pattern Recognition, established half a century ago during the early days of computer science, has since grown significantly in scope and influence.