Yang Ai, Yinhao Li, Yen-Wei Chen, Panyanat Aonpong, Xianhua Han
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ResMLP_GGR: Residual Multilayer Perceptrons- Based Genotype-Guided Recurrence Prediction of Non-small Cell Lung Cancer
Non-small Cell Lung Cancer (NSCLC) is one of the malignant tumors with the highest morbidity and mortality. The postoperative recurrence rate in patients with NSCLC is high, which directly endangers the lives of patients. In recent years, many studies have used Computed Tomography (CT) images to predict NSCLC recurrence. Although this approach is inexpensive, it has low prediction accuracy. Gene expression data can achieve high accuracy. However, gene acquisition is expensive and invasive, and cannot meet the recurrence prediction requirements of all patients. In this study, a low-cost, high-accuracy residual multilayer perceptrons-based genotype-guided recurrence (ResMLP_GGR) prediction method is proposed that uses a gene estimation model to guide recurrence prediction. First, a gene estimation model is proposed to construct a mapping function of mixed features (handcrafted and deep features) and gene data to estimate the genetic information of tumor heterogeneity. Then, from gene estimation data obtained using a regression model, representations related to recurrence are learned to realize NSCLC recurrence prediction. In the testing phase, NSCLC recurrence prediction can be achieved with only CT images. The experimental results show that the proposed method has few parameters, strong generalization ability, and is suitable for small datasets. Compared with state-of-the-art methods, the proposed method significantly improves recurrence prediction accuracy by 3.39% with only 1% of parameters.
中国图象图形学报Computer Science-Computer Graphics and Computer-Aided Design
CiteScore
1.20
自引率
0.00%
发文量
6776
期刊介绍:
Journal of Image and Graphics (ISSN 1006-8961, CN 11-3758/TB, CODEN ZTTXFZ) is an authoritative academic journal supervised by the Chinese Academy of Sciences and co-sponsored by the Institute of Space and Astronautical Information Innovation of the Chinese Academy of Sciences (ISIAS), the Chinese Society of Image and Graphics (CSIG), and the Beijing Institute of Applied Physics and Computational Mathematics (BIAPM). The journal integrates high-tech theories, technical methods and industrialisation of applied research results in computer image graphics, and mainly publishes innovative and high-level scientific research papers on basic and applied research in image graphics science and its closely related fields. The form of papers includes reviews, technical reports, project progress, academic news, new technology reviews, new product introduction and industrialisation research. The content covers a wide range of fields such as image analysis and recognition, image understanding and computer vision, computer graphics, virtual reality and augmented reality, system simulation, animation, etc., and theme columns are opened according to the research hotspots and cutting-edge topics.
Journal of Image and Graphics reaches a wide range of readers, including scientific and technical personnel, enterprise supervisors, and postgraduates and college students of colleges and universities engaged in the fields of national defence, military, aviation, aerospace, communications, electronics, automotive, agriculture, meteorology, environmental protection, remote sensing, mapping, oil field, construction, transportation, finance, telecommunications, education, medical care, film and television, and art.
Journal of Image and Graphics is included in many important domestic and international scientific literature database systems, including EBSCO database in the United States, JST database in Japan, Scopus database in the Netherlands, China Science and Technology Thesis Statistics and Analysis (Annual Research Report), China Science Citation Database (CSCD), China Academic Journal Network Publishing Database (CAJD), and China Academic Journal Network Publishing Database (CAJD). China Science Citation Database (CSCD), China Academic Journals Network Publishing Database (CAJD), China Academic Journal Abstracts, Chinese Science Abstracts (Series A), China Electronic Science Abstracts, Chinese Core Journals Abstracts, Chinese Academic Journals on CD-ROM, and China Academic Journals Comprehensive Evaluation Database.