Multimodal Visual Language Model For Lung Cancer Diagnosis
DOI:
https://doi.org/10.29284/9bcq0n14Keywords:
Multimodal deep learning, lung cancer diagnosis, cross-modal attention, Vision Transformer, ClinicalBERT, DNABERT, 3D-CNN, adaptive fusion network, missing modality imputation, clinical decision support, medical AI.Abstract
Accurate lung cancer diagnosis demands simultaneous synthesis of radiological imaging, histopathology, genomic profiles, and clinical documentation. Prevailing artificial intelligence systems remain constrained to single- or dual-modality integration, failing to exploit the synergistic diagnostic value latent across complementary data streams. This paper proposes the Cross-Modal Adaptive Fusion Network (CAFN), which concurrently processes four clinical modalities through specialized deep learning encoders: a 3D Convolutional Neural Network (3D-CNN) for volumetric CT scans, a Vision Transformer (ViT) for whole-slide histopathology images, DNABERT for genomic nucleotide sequences, and ClinicalBERT for electronic health record narratives. A dedicated Adaptive Fusion Network integrates these heterogeneous representations through four coordinated mechanisms: Cross-Modal Attention, Modality Gating, Dynamic Weighting, and Missing Modality Imputation, achieving robust, patient-specific diagnostic inference even when some modalities are absent. Experiments conducted on four publicly available benchmark datasets—MIMIC-III, LIDC-IDRI, NCT-CRC-HE-100K, and TCGA-LUAD—yield a fusion AUC of 0.991, classification accuracy of 95%, sensitivity of 93%, and specificity of 91%, representing a 15–25% improvement over state-of-the-art dual-modality systems. A modular, encoder-independent training strategy reduces peak computational complexity by 40–60% versus end-to-end architectures while eliminating the strict requirement for patient-level cross-modal data alignment.
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This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
