MuRL: Multi-Resolution Deep Reinforcement Learning for Melanoma Diagnosis From Dermoscopic Images

Area of Science:

• Artificial Intelligence in Dermatology
• Medical Image Analysis
• Computational Pathology

Background:

• Accurate and efficient melanoma diagnosis is critical for AI-driven clinical decision-making in dermatology.
• Current methods face challenges in achieving high diagnostic accuracy with computational efficiency.
• High computational resources are often required for precise melanoma classification.

Purpose of the Study:

• To propose MuRL, a novel reinforcement learning framework for accurate and efficient melanoma diagnosis from dermoscopic images.
• To enhance feature representation by integrating information from multiple spatial resolutions.
• To reduce computational costs through a dynamic inference strategy.

Main Methods:

• Developed MuRL, a reinforcement learning framework for melanoma diagnosis.
• Integrated informative features from multiple spatial resolutions for enhanced feature representation.
• Implemented a recurrent lesion-aware region localization to focus on diagnostically relevant areas.
• Introduced a dynamic inference strategy to adaptively terminate the inference process and reduce computational cost.

Main Results:

• Demonstrated the effectiveness of MuRL in accurate melanoma diagnosis on three public datasets.
• Showcased the efficiency of the proposed method in reducing computational requirements.
• Validated the integration of multi-resolution features and lesion-aware localization for improved performance.

Conclusions:

• MuRL offers an effective and efficient solution for AI-driven melanoma diagnosis from dermoscopic images.
• The framework successfully combines global context and fine-grained details for enhanced diagnostic accuracy.
• The dynamic inference strategy contributes to reduced computational load without compromising performance.