Low-dose CT Denoising with Language-engaged Dual-space Alignment: Enhancing LDCT Denoising Models with LEDA

Основные понятия

The author proposes the Language-Engaged Dual-space Alignment (LEDA) loss to optimize low-dose CT denoising models by aligning denoised CT and normal dose CT images in both continuous perceptual space and discrete semantic space.

Аннотация

The content introduces the LEDA loss for enhancing LDCT denoising models, leveraging large language models for alignment. The proposed method involves pretraining an LLM-guided CT autoencoder, quantizing features into text tokens, and minimizing discrepancies between denoised and NDCT images. Experimental results demonstrate improved image quality and explainability through language-level understanding.

Key points:

• Introduction of LEDA loss for LDCT denoising.
• Pretraining an LLM-guided CT autoencoder.
• Minimizing discrepancies between denoised and NDCT images.
• Experimental validation of enhanced image quality and explainability through language-level understanding.

Статистика

Extensive experimental results on two public LDCT denoising datasets demonstrate that our LEDA can enhance existing denoising models in terms of quantitative metrics and qualitative evaluation.
Source code is available at https://github.com/hao1635/LEDA.

Цитаты

"We propose an LEDA loss to supervise LDCT denoising, which maximizes the similarity between the NDCT and denoised LDCT images."
"Our LEDA helps understand the anatomical semantic information in the denoised image with quantized text tokens during the denoising process."

Ключевые выводы из

Low-dose CT Denoising with Language-engaged Dual-space Alignment

by Zhihao Chen,... в arxiv.org 03-12-2024

https://arxiv.org/pdf/2403.06128.pdf

Дополнительные вопросы

How can the integration of language models impact other areas of medical imaging beyond CT denoising

The integration of language models in medical imaging, as demonstrated in the context of CT denoising, can have far-reaching implications beyond just improving image quality. One significant impact is in the realm of image interpretation and analysis. By leveraging large language models (LLMs) to align images in both continuous perceptual space and discrete semantic space, we can potentially enhance automated diagnosis processes. LLMs can aid in extracting meaningful information from medical images, enabling more accurate identification of abnormalities or subtle features that may not be easily discernible by traditional methods.
Furthermore, the incorporation of language models could revolutionize how medical professionals interact with imaging data. It opens up possibilities for natural language interfaces where clinicians can query the system using plain language to retrieve relevant information from images quickly. This streamlined communication between humans and machines has the potential to improve workflow efficiency and facilitate better decision-making.
Additionally, LLMs could play a crucial role in standardizing terminology and annotations across different healthcare systems. By providing a common framework for understanding and describing medical images, LLMs can promote interoperability and consistency in data interpretation. This standardized approach could lead to improved collaboration among healthcare providers and researchers working on diverse imaging datasets.

What potential challenges or limitations could arise from relying heavily on large language models for medical imaging tasks

While integrating large language models into medical imaging tasks offers numerous benefits, there are also potential challenges and limitations that need to be considered:

Computational Resources: Large language models require substantial computational resources for training and inference. Implementing these models at scale for real-time applications like medical imaging may pose challenges due to high computational costs.

Data Privacy Concerns: Medical imaging data is sensitive and subject to strict privacy regulations like HIPAA. Utilizing LLMs raises concerns about patient data privacy since these models have vast capacity for learning intricate details from the data they are trained on.

Interpretability: Despite their impressive performance, LLMs often lack interpretability which is critical in healthcare settings where decisions impact patient outcomes directly.

4 .Generalization: Language models trained on specific datasets may struggle with generalization when applied to new or unseen data types or modalities within medical imaging.
5 .Bias: There's a risk of bias being perpetuated through large language model training if not carefully monitored or mitigated.

How might incorporating language-level explainability influence patient outcomes or clinical decision-making processes

Incorporating language-level explainability into medical imaging tasks such as CT denoising can have profound implications for patient outcomes and clinical decision-making processes:
1 .Enhanced Communication: Language-level explanations generated by AI systems provide clinicians with transparent insights into how decisions are made based on image analysis results.
2 .Improved Trust: Understanding why an AI system arrived at a particular conclusion fosters trust among healthcare professionals towards adopting AI technologies.
3 .Educational Tool: Explainable AI powered by language modeling serves as an educational tool helping clinicians understand complex algorithms behind diagnostic recommendations derived from image analyses.
4 .Error Detection: The ability to trace back decisions made by AI systems through explainable outputs facilitates error detection early-on ensuring patient safety remains paramount during diagnostics procedures
5 Clinical Decision Support: Accessible explanations enable physicians without deep technical expertise to make informed decisions based on machine-generated insights leadingto more accurate diagnosesand personalized treatment plans