Mitigating Biases in Cardiac Imaging through Controlled Latent Diffusion Models

Core Concepts

Addressing dataset imbalances and biases in cardiac magnetic resonance imaging (CMR) through the generation of synthetic data using a controlled latent diffusion model.

Abstract

The authors propose a method to alleviate imbalances and biases in cardiac magnetic resonance imaging (CMR) datasets by generating synthetic data using a controlled latent diffusion model. The key highlights are:

They adopt the ControlNet architecture based on a denoising diffusion probabilistic model to condition the image generation on text assembled from patient metadata (sex, age, BMI, health condition) and cardiac geometry derived from segmentation masks.

The authors evaluate the realism of the generated images using the Fréchet Inception Distance (FID) score and assess the impact of the synthetic data on a downstream heart failure classification task.

Experiments demonstrate the effectiveness of the proposed approach in mitigating dataset imbalances, such as the scarcity of younger patients or individuals with normal BMI level suffering from heart failure.

The authors conduct all experiments using a single, consumer-level GPU to highlight the feasibility of their approach within resource-constrained environments.

The generated synthetic data helps improve the performance and fairness of the heart failure classification model, particularly for underrepresented subgroups.

Overall, this work represents a significant step towards the adoption of synthetic data for the development of fair and generalizable models for medical classification tasks.

Stats

Cardiovascular diseases account for approximately one third of annual deaths globally.
The UK Biobank imaging study dataset used in this work consists of 25,480 multi-slice, short-axis cine CMRs with annotations for end-diastole (ED) and end-systole (ES) frames.
From this dataset, 270 patients were identified as diagnosed with heart failure.
The dataset was divided into subgroups based on sex, age, BMI, and health condition to analyze biases.

Quotes

"Debiasing Cardiac Imaging with Controlled Latent Diffusion Models"
"To address this issue, we propose a method to alleviate imbalances inherent in datasets through the generation of synthetic data based on sensitive attributes such as sex, age, body mass index, and health condition."
"Our experiments demonstrate the effectiveness of the proposed approach in mitigating dataset imbalances, such as the scarcity of younger patients or individuals with normal BMI level suffering from heart failure."

Key Insights Distilled From

Debiasing Cardiac Imaging with Controlled Latent Diffusion Models

by Grzegorz Sko... at arxiv.org 03-29-2024

https://arxiv.org/pdf/2403.19508.pdf

Deeper Inquiries

How can the proposed approach be extended to other medical imaging modalities beyond cardiac MRI to address dataset biases

The proposed approach can be extended to other medical imaging modalities beyond cardiac MRI by adapting the methodology to suit the specific characteristics of each modality. For instance, in the case of dermatology imaging, text-based prompts could include information about skin type, lesion characteristics, or patient demographics. Segmentation masks could be tailored to highlight relevant structures or abnormalities specific to dermatological conditions. By customizing the prompts and masks to align with the unique features of each imaging modality, synthetic data generation can be optimized to address dataset biases effectively.

What are the potential limitations of using text-based prompts and segmentation masks to guide the generation of synthetic medical images, and how can these be addressed

One potential limitation of using text-based prompts and segmentation masks is the risk of oversimplifying the complex relationships between patient attributes and imaging features. Text prompts may not capture all relevant information, leading to incomplete conditioning of the generative model. Similarly, segmentation masks may not fully represent the intricacies of anatomical structures or pathological findings, potentially limiting the diversity and accuracy of the synthetic images. To address these limitations, incorporating additional data sources such as clinical notes, genetic information, or multi-modal imaging data could provide a more comprehensive input for guiding synthetic image generation. Moreover, refining the segmentation algorithms to enhance the fidelity of the masks and integrating natural language processing techniques to extract nuanced information from textual inputs can improve the quality and relevance of the synthetic images.

Given the resource-constrained nature of the experiments, how can the scalability and computational efficiency of the proposed method be further improved to enable its widespread adoption in clinical settings

To enhance the scalability and computational efficiency of the proposed method for widespread adoption in clinical settings, several strategies can be implemented. Firstly, leveraging distributed computing resources or cloud-based platforms can facilitate parallel processing and reduce the training time of the generative models. Implementing optimized algorithms and model architectures tailored for efficient GPU utilization can further enhance computational efficiency. Additionally, exploring transfer learning techniques to fine-tune pre-trained generative models on specific medical imaging datasets can expedite the training process and reduce resource requirements. Furthermore, developing lightweight versions of the generative models or utilizing model compression techniques can enable deployment on edge devices or low-power hardware, making the approach more accessible in resource-constrained environments.