Deeply Dual Supervised learning for melanoma recognition

TL;DR

This paper introduces a Deeply Dual Supervised Learning framework that combines local and global features with attention mechanisms to improve melanoma recognition accuracy in skin lesion images.

Contribution

It proposes a novel dual-pathway model with attention and multi-scale features, enhancing detection of subtle melanoma cues over existing methods.

Findings

Outperforms state-of-the-art melanoma detection methods

Achieves higher accuracy and robustness across datasets

Effectively identifies subtle visual cues in skin images

Abstract

As the application of deep learning in dermatology continues to grow, the recognition of melanoma has garnered significant attention, demonstrating potential for improving diagnostic accuracy. Despite advancements in image classification techniques, existing models still face challenges in identifying subtle visual cues that differentiate melanoma from benign lesions. This paper presents a novel Deeply Dual Supervised Learning framework that integrates local and global feature extraction to enhance melanoma recognition. By employing a dual-pathway structure, the model focuses on both fine-grained local features and broader contextual information, ensuring a comprehensive understanding of the image content. The framework utilizes a dual attention mechanism that dynamically emphasizes critical features, thereby reducing the risk of overlooking subtle characteristics of melanoma. Additionally, we introduce a multi-scale feature aggregation strategy to ensure robust performance across varying image resolutions. Extensive experiments on benchmark datasets demonstrate that our framework significantly outperforms state-of-the-art methods in melanoma detection, achieving higher accuracy and better resilience against false positives. This work lays the foundation for future research in automated skin cancer recognition and highlights the effectiveness of dual supervised learning in medical image analysis.