Learning from Limited and Incomplete Data: A Multimodal Framework for Predicting Pathological Response in NSCLC
Major pathological response (pR) following neoadjuvant therapy is a clinically meaningful endpoint in non-small cell lung cancer, strongly associated with improved survival. However, accurate preoperative prediction of pR remains challenging, particularly in real-world clinical settings characterized by limited data availability and incomplete clinical profiles. In this study, we propose a multimodal deep learning framework designed to address these constraints by integrating foundation model-based CT feature extraction with a missing-aware architecture for clinical variables. This approach enables robust learning from small cohorts while explicitly modeling missing clinical information, without relying on conventional imputation strategies. A weighted fusion mechanism is employed to leverage the complementary contributions of imaging and clinical modalities, yielding a multimodal model that consistently outperforms both unimodal imaging and clinical baselines. These findings underscore the added value of integrating heterogeneous data sources and highlight the potential of multimodal, missing-aware systems to support pR prediction under realistic clinical conditions.
Code (0)
등록된 구현이 없습니다.
Tasks
Multimodal Deep LearningSimilar Papers 제목 키워드 기반
TIP: Tabular-Image Pre-training for Multimodal Classification with Incomplete Data
Images and structured tables are essential parts of real-world databases. Though tabular-image representation learning is promising to create new insights, it remains a challenging task, as tabular data is typically hete…
Contrastive Learningmultimodal interactionRepresentation LearningSelf-Supervised LearningGenerating with Fairness: A Modality-Diffused Counterfactual Framework for Incomplete Multimodal Recommendations
Incomplete scenario is a prevalent, practical, yet challenging setting in Multimodal Recommendations (MMRec), where some item modalities are missing due to various factors. Recently, a few efforts have sought to improve …
counterfactualFairnessMultimodal RecommendationConditional Evidence Reconstruction and Decomposition for Interpretable Multimodal Diagnosis
Neurobiological and neurodegenerative diseases are inherently multifactorial, arising from coupled influences spanning genetic susceptibility, brain alterations, and environmental and behavioral factors. Multimodal model…
Predicting Immune Biomarkers with MultiModal Mixture-of-Expert Pathology Foundation Models Empowers Precision Oncology
Predicting immune biomarkers associated with the tumor immune microenvironment (TIME) is critical for advancing precision oncology, yet existing approaches are largely limited to single image modalities and suffer from i…
Retrieval-Augmented Dynamic Prompt Tuning for Incomplete Multimodal Learning
Multimodal learning with incomplete modality is practical and challenging. Recently, researchers have focused on enhancing the robustness of pre-trained MultiModal Transformers (MMTs) under missing modality conditions by…
ImputationRetrieval