A Clinical-oriented Multi-level Contrastive Learning Method for Accurate Disease Diagnosis from Low-quality Medical Images

Alapfogalmak

A clinical-oriented multi-level contrastive learning framework that enhances the model's capacity to extract lesion features and discriminate between lesion and low-quality factors, enabling more accurate disease diagnosis from low-quality medical images.

Kivonat

The paper proposes a clinical-oriented multi-level contrastive learning (CoMCL) framework for disease diagnosis from low-quality medical images. The key highlights are:

Construction of multi-level positive and negative pairs:

Enhances the model's ability to distinguish low-quality factors from lesions in low-quality medical images.
Improves the model's capability to discriminate between lesion and non-lesion areas.
Enhances the model's awareness to identify lesion characteristics in low-quality images.

Introduction of a dynamic hard sample mining method based on self-paced learning:

Effectively leverages hard negatives to improve the quality of the learned lesion-related embeddings.

The proposed CoMCL framework is validated on two public medical image datasets, EyeQ and Chest X-ray, demonstrating superior performance compared to other state-of-the-art disease diagnostic methods, especially in the presence of low-quality factors.

Statisztikák

Medical images often suffer from various low-quality factors such as artifacts and blurring, leading to quality degradation.
Low-quality factors may cause contrastive learning to incorrectly pull the distance in the embedding space between lesion samples and low-quality healthy samples, or between healthy samples and low-quality lesion samples, thereby degrading the diagnostic performance.

Idézetek

"To address these challenges in real clinical settings and fully exploit medical images without pixel-level annotations, some existing studies [11, 15,24] proactively explore the impact of contrastive learning (CL) [4, 8] on automated disease diagnosis models. However, they do not fully consider the common quality variations in medical images, which limits their effectiveness in eliminating the interference of low-quality factors on disease diagnosis."
"The challenge mentioned above motivates us to develop a Clinical-oriented Multi-level Contrastive Learning method, named CoMCL, tailored for automatic disease diagnosis on low-quality medical images."

Főbb Kivonatok

A Clinical-oriented Multi-level Contrastive Learning Method for Disease Diagnosis in Low-quality Medical Images

by Qingshan Hou... : arxiv.org 04-09-2024

https://arxiv.org/pdf/2404.04887.pdf

Mélyebb kérdések

How can the proposed multi-level contrastive learning framework be extended to other medical imaging modalities beyond fundus and chest X-ray images

The proposed multi-level contrastive learning framework can be extended to other medical imaging modalities beyond fundus and chest X-ray images by adapting the construction of positive and negative pairs to suit the characteristics of different imaging modalities. For instance, in MRI or CT scans, where the image quality may vary due to factors like noise or artifacts, the framework can be modified to incorporate multi-level positive and negative pairs that account for these variations. Additionally, the self-paced learning-based dynamic sampling method can be adjusted to handle the specific challenges present in each modality, such as different types of lesions or abnormalities.

What are the potential limitations of the self-paced learning-based dynamic sampling method, and how can it be further improved to handle more complex medical image datasets

One potential limitation of the self-paced learning-based dynamic sampling method is the sensitivity to the selection of hyperparameters, such as the adaptive parameter Kt. If not appropriately tuned, the method may struggle to effectively mine hard negatives, leading to suboptimal performance. To address this limitation, further research could focus on developing automated techniques for determining the optimal hyperparameters based on the characteristics of the dataset. Additionally, exploring more sophisticated strategies for sampling hard negatives, such as incorporating domain-specific knowledge or leveraging ensemble techniques, could enhance the method's robustness and adaptability to complex medical image datasets.

Given the clinical significance of disease diagnosis, how can the CoMCL framework be integrated into real-world healthcare systems to enhance the efficiency and accuracy of disease diagnosis workflows

To integrate the CoMCL framework into real-world healthcare systems and enhance the efficiency and accuracy of disease diagnosis workflows, several steps can be taken. Firstly, collaboration with healthcare institutions and medical professionals is essential to validate the framework's performance in clinical settings and ensure its alignment with diagnostic protocols. Secondly, the framework can be integrated into existing medical imaging software or platforms to streamline the diagnosis process and provide real-time feedback to clinicians. Moreover, continuous monitoring and feedback loops can be established to refine the framework based on clinical outcomes and user feedback, ensuring its effectiveness and usability in practice. Finally, incorporating interpretability features into the framework to explain the reasoning behind diagnostic decisions can enhance trust and acceptance among healthcare providers.