Lung cancer, being one of the most prevalent malignant tumors, carries the highest incidence and mortality rates among all cancers. The severity of lung cancer poses a significant threat to human health, underscoring the critical importance of early tumor diagnosis for improving patient survival rates. Current diagnostic methods primarily rely on doctors' clinical experience, coupled with various detection techniques such as laboratory examinations and imaging analysis, to make comprehensive assessments. With the integration of data analysis technology and biomedicine, computer-aided modeling methods have found applications in the medical field. Many of these approaches transform tumor diagnosis into classification problems and employ regression analysis, decision trees, deep learning, and related technologies to analyze and extract insights from single-modal data, like laboratory test results or medical images. Such efforts aim to enhance the accuracy of results obtained from different detection methods. However, the clinical diagnosis of lung tumors is a complex process that demands comprehensive analysis and mining of all modal information pertaining to patients. Multimodal learning methods can effectively complement and fuse diverse modal information, enabling comprehensive semantic descriptions of features. Consequently, there is a need to explore new multimodal medical data analysis techniques that integrate signs, symptoms, medical test results, and other multimodal information of lung tumor patients to facilitate auxiliary diagnosis.
This Research Topic invites high-quality papers from academics and industry-related researchers working on multimodal learning. The goal is to present the latest advancements in methods and applications that contribute to promising multimodal medical data analysis for lung tumor diagnosis (LTD). We encourage researchers to contribute their innovative work to advance the field of multimodal data analysis and its application in LTD. Submissions to this Research Topic should represent original, unpublished research that delves into in-depth investigations. The suggested topics include, but are not limited to:
• Artificial Intelligence Theory and Methods for LTD
• Multimodal Analysis for LTD
• Domain Adaptation and Transfer Learning for LTD
• Machine Learning and Reinforcement Learning for LTD
• Cross-modal Analysis for LTD
• Small Sample Learning for LTD
• Uncertainty Data Analysis for LTD
• Low-quality Data Analysis for LTD
• Multimodal LTD Datasets and Applications
• Other Tumor Diagnostic Methods Beneficial for LTD
Lung cancer, being one of the most prevalent malignant tumors, carries the highest incidence and mortality rates among all cancers. The severity of lung cancer poses a significant threat to human health, underscoring the critical importance of early tumor diagnosis for improving patient survival rates. Current diagnostic methods primarily rely on doctors' clinical experience, coupled with various detection techniques such as laboratory examinations and imaging analysis, to make comprehensive assessments. With the integration of data analysis technology and biomedicine, computer-aided modeling methods have found applications in the medical field. Many of these approaches transform tumor diagnosis into classification problems and employ regression analysis, decision trees, deep learning, and related technologies to analyze and extract insights from single-modal data, like laboratory test results or medical images. Such efforts aim to enhance the accuracy of results obtained from different detection methods. However, the clinical diagnosis of lung tumors is a complex process that demands comprehensive analysis and mining of all modal information pertaining to patients. Multimodal learning methods can effectively complement and fuse diverse modal information, enabling comprehensive semantic descriptions of features. Consequently, there is a need to explore new multimodal medical data analysis techniques that integrate signs, symptoms, medical test results, and other multimodal information of lung tumor patients to facilitate auxiliary diagnosis.
This Research Topic invites high-quality papers from academics and industry-related researchers working on multimodal learning. The goal is to present the latest advancements in methods and applications that contribute to promising multimodal medical data analysis for lung tumor diagnosis (LTD). We encourage researchers to contribute their innovative work to advance the field of multimodal data analysis and its application in LTD. Submissions to this Research Topic should represent original, unpublished research that delves into in-depth investigations. The suggested topics include, but are not limited to:
• Artificial Intelligence Theory and Methods for LTD
• Multimodal Analysis for LTD
• Domain Adaptation and Transfer Learning for LTD
• Machine Learning and Reinforcement Learning for LTD
• Cross-modal Analysis for LTD
• Small Sample Learning for LTD
• Uncertainty Data Analysis for LTD
• Low-quality Data Analysis for LTD
• Multimodal LTD Datasets and Applications
• Other Tumor Diagnostic Methods Beneficial for LTD