Comparison of Loss Functions for Robust Deep Learning-based Echocardiography Segmentation when Learning with Partially Labelled Data from Multiple Domains
arXiv:2607.05008v1 Announce Type: cross Abstract: Echocardiography is the first imaging modality used for assessing cardiac function, and accurate segmentation of cardiac structures is essential for deriving biomarkers. However, the development of effective automated segmentation models for multiple cardiac structures is challenged by the difficulty of training on datasets from different sources that are often partially-labelled. This study aims to address this challenge by evaluating the performance of three loss functions - adaptive categorical cross entropy (aCCE) loss, marginal loss, and t
This research addresses a critical challenge in medical AI by improving the robustness of deep learning models for echocardiography, which is crucial for widespread clinical adoption.
Accurate and robust AI-powered medical diagnostics, especially in areas like cardiac health, will lead to improved patient outcomes and more efficient healthcare systems, reducing reliance on manual analysis.
The development of more resilient AI models for medical imaging, capable of handling diverse and partially labeled datasets, reduces deployment barriers for AI in sensitive healthcare applications.
- · AI-driven medical diagnostic companies
- · Healthcare providers
- · Patients
Improved accuracy and reliability of automated cardiac diagnostics.
Faster and more consistent assessment of cardiac function in clinical settings.
Potential for broader integration of AI across various medical imaging modalities, accelerating diagnosis and treatment.
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