Efficient Single-Shared Network with Biased Loss for Parameter-Efficient Multi-Modal Skin Lesion Classification

แนวคิดหลัก

A parameter-efficient multi-modal (PEMM) framework that utilizes a single-shared network, shared cross-attention modules, and a biased loss function to achieve state-of-the-art classification performance with fewer parameters compared to current advanced methods.

บทคัดย่อ

The paper introduces a novel Parameter-Efficient Multi-Modal (PEMM) framework for skin lesion classification. The key components of the PEMM framework are:

Single-Shared Network (SSN): The PEMM framework employs a single-shared encoder to extract features from both clinical and dermoscopy images, while maintaining individual classifiers for the two modalities. This parameter-sharing scheme significantly reduces the model's parameters compared to using two separate encoders.

Shared Cross-Attention (SCA) Module: The PEMM framework introduces a new shared cross-attention module to efficiently integrate multimodal features at different layers, further reducing the number of parameters compared to commonly-used cross-attention mechanisms.

Biased Loss (BL) Function: Inspired by the prior knowledge that dermoscopy images provide more useful information for diagnosis than clinical images, the PEMM framework proposes a new biased loss function. This function guides the single-shared network to focus more on the dermoscopy branch and learn a better joint feature representation for the modal-specific classification task.

Extensive experiments on the SPC dataset and a collected ISIC dataset demonstrate that the PEMM framework outperforms current state-of-the-art methods in both accuracy and model parameter efficiency. The PEMM framework achieves the highest Avg AUC of 87.6% and Avg ACC of 77.4% on the SPC dataset, while using approximately 60% fewer parameters compared to the second-best methods.

สถิติ

The proposed PEMM framework achieves the highest Avg AUC of 87.6% and Avg ACC of 77.4% on the SPC dataset.
The PEMM framework uses approximately 60% fewer parameters compared to the second-best methods.

คำพูด

"We validated that both clinical and dermoscopy modalities can be input into a single-shared network with strong capacity, achieving similar performance while reducing a large number of parameters compared to commonly-used two individual networks."
"We introduced a new shared cross-attention module to efficiently integrate multimodal features at different layers."
"We propose a novel prior-biased loss that guides the single-shared network to learn more meaningful information for accurate diagnosis."

ข้อมูลเชิงลึกที่สำคัญจาก

Single-Shared Network with Prior-Inspired Loss for Parameter-Efficient Multi-Modal Imaging Skin Lesion Classification

by Peng Tang,To... ที่ arxiv.org 03-29-2024

https://arxiv.org/pdf/2403.19203.pdf

สอบถามเพิ่มเติม

How can the PEMM framework be extended to other multi-modal medical imaging tasks beyond skin lesion classification

The PEMM framework can be extended to other multi-modal medical imaging tasks beyond skin lesion classification by adapting the shared network architecture and fusion strategies to suit the specific characteristics of the new tasks. For instance, in tasks such as tumor detection using MRI and PET scans, the shared network can be designed to extract features from both modalities efficiently. The fusion strategies can be tailored to combine the information from MRI and PET scans effectively, considering the unique characteristics of each modality. Additionally, the biased loss function can be adjusted based on the relative importance of the modalities in the new task, ensuring that the model prioritizes the most relevant information for accurate classification. By customizing the PEMM framework to different multi-modal medical imaging tasks, it can achieve parameter efficiency and high classification performance across a variety of applications.

What are the potential limitations of the biased loss function, and how can it be further improved to better balance the importance of clinical and dermoscopy information

The biased loss function in the PEMM framework may have potential limitations in scenarios where the relative importance of clinical and dermoscopy information varies significantly across different datasets or tasks. To address this limitation, the biased loss function can be further improved by incorporating adaptive weighting mechanisms based on the dataset characteristics or task requirements. For example, instead of using a fixed weight factor for clinical and dermoscopy information, dynamic weighting schemes can be implemented that adjust the weights during training based on the model's performance. Additionally, incorporating regularization techniques or ensemble methods to fine-tune the biased loss function can help achieve a better balance between the importance of clinical and dermoscopy information in the classification task. By enhancing the flexibility and adaptability of the biased loss function, the PEMM framework can better address the varying importance of modalities in different contexts.

Could the PEMM framework be adapted to handle more than two modalities, and how would that affect the parameter efficiency and classification performance

The PEMM framework can be adapted to handle more than two modalities by extending the shared network architecture and fusion strategies to accommodate the additional modalities. When incorporating multiple modalities, the parameter efficiency and classification performance of the framework may be affected depending on the complexity and interplay of the modalities. To maintain parameter efficiency, techniques such as hierarchical feature extraction, modular design, and attention mechanisms can be employed to effectively integrate multiple modalities while minimizing the increase in parameters. Additionally, optimizing the biased loss function to consider the relative importance of each modality in the multi-modal task can help ensure that the model focuses on the most informative features for accurate classification. By expanding the PEMM framework to handle more than two modalities, it can provide a versatile and efficient solution for a wide range of multi-modal medical imaging tasks.