Transformer-CNN Fused Architecture for Enhanced Skin Lesion Segmentation (2401.05481v1)

Abstract: The segmentation of medical images is important for the improvement and creation of healthcare systems, particularly for early disease detection and treatment planning. In recent years, the use of convolutional neural networks (CNNs) and other state-of-the-art methods has greatly advanced medical image segmentation. However, CNNs have been found to struggle with learning long-range dependencies and capturing global context due to the limitations of convolution operations. In this paper, we explore the use of transformers and CNNs for medical image segmentation and propose a hybrid architecture that combines the ability of transformers to capture global dependencies with the ability of CNNs to capture low-level spatial details. We compare various architectures and configurations and conduct multiple experiments to evaluate their effectiveness.

• The paper introduces a dual-branch architecture that fuses CNN spatial features with transformer global context for enhanced skin lesion segmentation.
• It employs a ResNet-34 encoder and a DeiT-Small transformer integrated via a novel fusion module to achieve a Jaccard index of 0.795 on the ISIC 2017 dataset.
• The design reduces computational load and improves precision, paving the way for efficient deployment in clinical settings and further hybrid model research.

Enhanced Skin Lesion Segmentation through Transformer-CNN Fusion Architecture

Overview of the Proposed Architecture

In the domain of medical imaging, particularly skin lesion segmentation, there exists a pivotal challenge in effectively capturing both the global context and low-level spatial details within images. Traditional convolutional neural networks (CNNs), while adept at identifying spatial hierarchies, fall short in integrating comprehensive contextual information, a gap prominently addressed by transformers due to their global self-attention mechanism. The paper introduces a novel hybrid model that synergizes the strengths of CNNs and transformers to achieve enhanced segmentation performance. The architecture employs a dual-branch parallel approach, leveraging a CNN encoder for spatial features and a transformer-based network for global context, integrated through a sophisticated fusion module. This design not only mitigates the limitations associated with each individual model type but also presents a computationally efficient solution adaptable for low-resource environments.

The Dual-Branch Parallel Architecture

The core of the proposed architecture encompasses a CNN and a transformer branch processed in parallel. The CNN branch progressively captures nuanced spatial details, whereas the transformer branch, employing a global self-attention mechanism, ensures comprehensive context capture. A distinctive feature of this model is the fusion module, which adeptly merges the features extracted from both branches, facilitating a coherent, enriched feature representation essential for precise segmentation.

1. CNN Encoder: Utilizes a ResNet-34 backbone, progressively increasing the receptive field and retaining significant spatial information.
2. Transformer Network: Adopts a DeiT-Small configuration, emphasizing global context through a generalized encoder-decoder structure, where image patches are embedded, and spatial information is embedded through positional encodings.

Fusion Module and Feature Integration

The fusion module represents a novel element of the architecture, designed to intelligently amalgamate the attributes extracted from the CNN and transformer pathways. By employing mechanisms such as channel and spatial attention, along with convolutional layers to harmonize the feature maps, this module ensures that the integrated output capitalizes on both global and local cues. The model further innovates with attention-gated skip connections, enhancing the flow and integration of multi-scale features across the network, facilitating superior segmentation outcomes.

Empirical Evaluation and Findings

The architecture was rigorously evaluated on the ISIC 2017 dataset, a benchmark for skin lesion analysis. The model achieved a Jaccard index of 0.795, showcasing an improvement over existing state-of-the-art methods while necessitating fewer epochs for convergence, thus evidencing its computational efficiency and effectiveness. Notable findings include:

• Performance: The proposed model outperformed established benchmarks, affirming the viability of the dual-branch approach for medical image segmentation.
• Efficiency: With a streamlined structure requiring significantly fewer parameters and computational resources, the model underscores a practical solution adaptable for deployment in varied clinical settings.

Future Directions and Theoretical Implications

This paper underscores the potential of combining CNNs and transformers in a parallel configuration for medical image analysis. The architecture’s proficiency in capturing both detailed and contextual information paves the way for further exploration into hybrid models for a broader array of medical imaging tasks. Future investigations could explore:

• Generalization: Assessing the model’s applicability and performance across diverse datasets and segmentation challenges.
• Interpretability: Enhancing the model’s transparency to foster clinical trust and understanding.
• Optimization: Refining the architecture and training process for greater efficiency and accuracy.

Conclusion

The fusion of CNN and transformer architectures presents a promising avenue for advancing medical image segmentation, particularly for skin lesions. By harnessing the complementary strengths of these two powerful technologies, the proposed model achieves superior segmentation accuracy while maintaining computational efficiency. This research not only contributes a novel architecture to the field but also sets a precedence for future studies exploring the synergy between deep learning models in medical image analysis.