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引用次数: 0
摘要
大肠息肉是大肠癌的重要早期表现,对预防大肠癌意义重大。尽管及时发现和人工干预大肠息肉可以降低其癌变几率,但现有方法大多忽视了息肉的不确定性和位置问题,导致检测性能下降。针对这些问题,本文提出了一种通过 PAM-Net 进行息肉诊断的新型大肠图像分析方法。具体来说,我们设计了一个并行注意力模块来加强对大肠息肉图像的分析,以提高息肉的确定性。此外,我们的方法还引入了 GWD 损失,从息肉位置的角度提高了息肉诊断的准确性。大量实验结果表明,与 SOTA 基线相比,所提出的方法非常有效。这项研究提高了息肉检测的准确性,为临床医学中的息肉检测做出了贡献。
Colorectal polyp is an important early manifestation of colorectal cancer, which is significant for the prevention of colorectal cancer. Despite timely detection and manual intervention of colorectal polyps can reduce their chances of becoming cancerous, most existing methods ignore the uncertainties and location problems of polyps, causing a degradation in detection performance. To address these problems, in this paper, we propose a novel colorectal image analysis method for polyp diagnosis via PAM-Net. Specifically, a parallel attention module is designed to enhance the analysis of colorectal polyp images for improving the certainties of polyps. In addition, our method introduces the GWD loss to enhance the accuracy of polyp diagnosis from the perspective of polyp location. Extensive experimental results demonstrate the effectiveness of the proposed method compared with the SOTA baselines. This study enhances the performance of polyp detection accuracy and contributes to polyp detection in clinical medicine.
期刊介绍:
Frontiers in Computational Neuroscience is a first-tier electronic journal devoted to promoting theoretical modeling of brain function and fostering interdisciplinary interactions between theoretical and experimental neuroscience. Progress in understanding the amazing capabilities of the brain is still limited, and we believe that it will only come with deep theoretical thinking and mutually stimulating cooperation between different disciplines and approaches. We therefore invite original contributions on a wide range of topics that present the fruits of such cooperation, or provide stimuli for future alliances. We aim to provide an interactive forum for cutting-edge theoretical studies of the nervous system, and for promulgating the best theoretical research to the broader neuroscience community. Models of all styles and at all levels are welcome, from biophysically motivated realistic simulations of neurons and synapses to high-level abstract models of inference and decision making. While the journal is primarily focused on theoretically based and driven research, we welcome experimental studies that validate and test theoretical conclusions.
Also: comp neuro