Gene expression profiles obtained through DNA microarray have proven successful in providing critical information for cancer detection classifiers. However, the limited number of samples in these datasets poses a challenge to employ complex methodologies such as deep neural networks for sophisticated analysis. To address this "small data" dilemma, Meta-Learning has been introduced as a solution to enhance the optimization of machine learning models by utilizing similar datasets, thereby facilitating a quicker adaptation to target datasets without the requirement of sufficient samples. In this study, we present a meta-learning-based approach for predicting lung cancer from gene expression profiles. We apply this framework to well-established deep learning methodologies and employ four distinct datasets for the meta-learning tasks, where one as the target dataset and the rest as source datasets. Our approach is evaluated against both traditional and deep learning methodologies, and the results show the superior performance of meta-learning on augmented source data compared to the baselines trained on single datasets. Moreover, we conduct the comparative analysis between meta-learning and transfer learning methodologies to highlight the efficiency of the proposed approach in addressing the challenges associated with limited sample sizes. Finally, we incorporate the explainability study to illustrate the distinctiveness of decisions made by meta-learning.
Artificial Intelligence Algorithms for the Detection of Pathologies Related to Lung Cancer through Image Analysis using Convolutional Neural Networks and Data Augmentation: a systematic mapping of the literature
Reasoning Language Model for Personalized Lung Cancer Screening
Department of Biomedical Engineering, School of Engineering, Center for Computational Innovations, Center for Biotechnology & Interdisciplinary Studies（生物医学工程系、工程学院、计算创新中心、生物技术与跨学科研究所以及中心）
Identifying multi-omics interactions for lung cancer drug targets discovery using Kernel Machine Regression
Department of Information and Communication Technology, Mawlana Bhashani Science and Technology University, Santosh, Tangail, 1902, Dhaka, Bangladesh（信息与通信技术系，穆拉纳巴沙尼科学与技术大学）；Department of Computer Science, Tulane University, New Orleans, LA, USA（计算机科学系，杜兰大学）；Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), 138632, Singapore（生物信息学研究所（BII），科技研究局（A*STAR））；Tulane Center for Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA 70112, USA（杜兰大学生物医学信息学与基因组学中心，医学部）；Ochsner Center for Outcomes Research, Ochsner Research, New Orleans, LA 70121, USA（Ochsner研究中心，Ochsner研究）
Department of Computer Sc. & Engg., B. P. Poddar Institute of Management and Technology, Kolkata, India（印度加尔各答B. P. Poddar管理与技术学院计算机科学与工程系）；Department of Computer Sc. & Engineering, Aliah University, Kolkata, India（印度加尔各答阿利亚大学计算机科学与工程系）
Predicting Targeted Therapy Resistance in Non-Small Cell Lung Cancer Using Multimodal Machine Learning
Geisel School of Medicine at Dartmouth（达特茅斯盖泽尔医学院）；Ochsner Health System（奥克斯纳医疗系统）；Leiden University Medical Center（莱顿大学医学中心）；Dartmouth-Hitchcock Medical Center（达特茅斯-希区柯克医疗中心）；Dartmouth College（达特茅斯学院）
Multimodal Doctor-in-the-Loop: A Clinically-Guided Explainable Framework for Predicting Pathological Response in Non-Small Cell Lung Cancer
Research Unit of Computer Systems and Bioinformatics, Department of Engineering（计算机系统与生物信息学研究单位，工程系）；Research Unit of Radiation Oncology, Department of Medicine and Surgery（放射肿瘤学研究单位，医学与外科系）；Research Unit of Anatomical Pathology, Department of Medicine and Surgery（病理学研究单位，医学与外科系）
Fairness Evaluation of Risk Estimation Models for Lung Cancer Screening
Diagnostic Image Analysis Group, Department of Medical Imaging, Radboud University Medical Center（放射医学成像分析组，医学影像系，拉德堡德大学医学中心）；Department of Artificial Intelligence, Radboud University（人工智能系，拉德堡德大学）；Ethics of Healthcare Group, Department IQ Health, Radboud University Medical Center（医疗伦理组，IQ健康系，拉德堡德大学医学中心）
Evaluating LeNet Algorithms in Classification Lung Cancer from Iraq-Oncology Teaching Hospital/National Center for Cancer Diseases
Faculty of Electrical and Computer Engineering, Islamic Azad University（伊斯兰阿扎德大学电气与计算机工程学院）；Young Researchers and Elite Club, Ardabil Branch, Islamic Azad University（伊斯兰阿扎德大学阿尔达比尔分校青年研究者与精英俱乐部）；Iraq-Oncology Teaching Hospital/National Center for Cancer Diseases（伊拉克肿瘤学教学医院/国家癌症疾病中心）
Application of machine learning models to predict the relationship between air pollution, ecosystem degradation, and health disparities and lung cancer in Vietnam
Computer Science University, Vietnamese-German University（越南德国大学计算机科学大学）；Ho Chi Minh City University of Technology（胡志明市技术大学）
Stress-testing cross-cancer generalizability of 3D nnU-Net for PET-CT tumor segmentation: multi-cohort evaluation with novel oesophageal and lung cancer datasets
QIMR Berghofer Medical Research Institute（昆士兰医学研究 institute）；Australian Institute for Bioengineering and Nanotechnology（澳大利亚生物工程与纳米技术研究所）；The University of Queensland（昆士兰大学）；Frazer Institute（弗拉泽研究所）；Princess Alexandra Hospital（亚历山大公主医院）；Faculty of Health, Medicine and Behavioural Sciences（健康、医学与行为科学学院）；School of Biomedical Sciences（生物医学科学学院）；Uppsala University（乌普萨拉大学）；Rohtak Nuclear Medcare Imaging Therapy and Research Center（罗塔克核医学成像治疗与研究中心）
A 3D Probabilistic Deep Learning System for Detection and Diagnosis of Lung Cancer Using Low-Dose CT Scans
Draper（德雷珀实验室）；Google（谷歌）
我们介绍了一种新的计算机辅助检测和诊断系统，肺癌筛查与低剂量CT扫描，产生有意义的概率评估。我们的系统完全基于3D卷积神经网络，并在公开的LUNA16和Kaggle Data Science Bowl挑战赛上实现了肺结节检测和恶性肿瘤分类任务的最新性能。虽然结节检测系统通常是自行设计和优化的，但我们发现，考虑检测和诊断组件之间的耦合是很重要的。利用这种耦合使我们能够开发一种端到端系统，该系统具有更高和更强大的性能，并消除了对结节检测假阳性减少阶段的需要。此外，我们在我们的深度学习系统中描述了模型的不确定性，这是肺部CT分析的第一个，并表明我们可以使用它来为结节检测和患者恶性肿瘤诊断提供校准良好的分类概率。这些由模型不确定性提供信息的校准概率可用于后续基于风险的诊断干预或疾病治疗决策，正如我们使用基于概率的患者转诊策略进一步改善我们的结果所证明的那样。
Machine Learning-Assisted Recurrence Prediction for Early-Stage Non-Small-Cell Lung Cancer Patients
Accenture Labs（埃森哲实验室）；Hospital Universitario Puerta de Hierro Majadahonda（马哈拉达翁达普埃尔塔德希罗大学医院）；University of Galway（戈尔韦大学）；Insight Centre for Data Analytics（洞察数据分析中心）；Hospital General de Valencia（瓦伦西亚综合医院）；Hospital Universitario de Jaén（哈恩大学医院）；Masaryk University（马萨里克大学）；Masaryk Memorial Cancer Institute（马萨里克纪念癌症研究所）；Hospital General Universitario de Alicante（阿利坎特综合大学医院）；University College London（伦敦大学学院）；Complejo Hospitalario Universitario A Coruña（拉科鲁尼亚大学医院综合体）；Hospital Universitario de Salamanca（萨拉曼卡大学医院）
