A novel multi-agent spatiotemporal fusion framework for intelligent skin cancer diagnosis

TL;DR

A new AI framework improves skin cancer diagnosis by combining spatial and temporal data, leading to higher accuracy than existing methods.

Contribution

A novel multi-agent spatiotemporal fusion framework that outperforms existing models in skin cancer diagnosis.

Findings

The framework achieved 94.5% accuracy and 93.8% F1-score on dermoscopic datasets.

Temporal modeling and adaptive fusion significantly improved differentiation of early melanoma from benign lesions.

Abstract

Skin cancer is one of the most common malignancies worldwide, and early-stage diagnosis remains challenging due to its morphological similarity to benign lesions. Most existing computer-aided diagnostic systems rely on single static images, overlooking temporal information that is critical for distinguishing progressive malignancy.

We propose a novel multi-agent spatiotemporal fusion framework to enhance diagnostic accuracy. The framework consists of three key components: (1) a spatial agent based on a convolutional neural network for high-fidelity static feature extraction; (2) a temporal agent employing gated recurrent units to model longitudinal lesion evolution; and (3) a collaboration agent that dynamically fuses spatial and temporal representations via an attention-based weighting strategy.

Experiments on large-scale public dermoscopic datasets showed that our method achieved an accuracy of 94.5%, an F1-score of 93.8%, and an AUC of 0.97—outperforming traditional machine learning models, CNN classifiers, and 3D-CNN baselines. Ablation studies further confirmed the critical contribution of temporal modeling and adaptive fusion, particularly in differentiating early melanoma from atypical nevi.

This work highlights the potential of spatiotemporal modeling to improve early skin cancer detection and provides a promising direction for AI-assisted diagnosis of other chronic diseases requiring longitudinal monitoring.

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