JI-ADF: Joint–Individual Learning with Adaptive Decision Fusion for Multimodal Skin Lesion Classification

Phan Nguyen, Dat Cao, Hien Quang Kha, Hien Chu, Minh H. N. Le, Pham Quoc Thao Trang, Nguyen Quoc Khanh Le

Keywords: Multimodal Learning, Skin Lesion Classification, Adaptive Fusion, Dermoscopic Imaging, Clinical Metadata, Medical Image Analysis

Abstract: Skin lesion classification is essential for early dermatological diagnosis, yet many existing computer-aided systems rely primarily on dermoscopic images and underutilize the multimodal evidence routinely available in clinical practice. To address this gap, we propose \textbf{JI-ADF}, a trimodal deep learning framework that integrates dermoscopic images, clinical photographs, and structured patient metadata for clinically grounded skin lesion classification. The proposed architecture combines joint multimodal representation learning with modality-specific auxiliary supervision and an adaptive decision fusion mechanism that dynamically calibrates modality contributions on a per-sample basis. To enhance cross-modal reasoning while preserving modality-specific evidence, we further introduce a multimodal fusion attention (MMFA) module. We evaluate JI-ADF on the large-scale MILK10k benchmark. Extensive analyses, including modality ablation, calibration evaluation, and Grad-CAM visualization, further confirm the robustness and clinically meaningful behavior of the model. The results indicate that JI-ADF provides a reliable and practical foundation for multimodal skin lesion classification in real-world clinical settings.

Submission Number: 39