# Representation learning and information fusion for the population analysis of cardiac function

Nicolas Duchateau

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Nicolas Duchateau. Representation learning and information fusion for the population analysis of cardiac function. International Symposium on Computer Methods in Biomechanics and Biomedical Engineering (CMBBE), 2025, Barcelone, Spain. hal-05249795

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Submitted on 11 Sep 2025

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# REPRESENTATION LEARNING AND INFORMATION FUSION FOR THE POPULATION ANALYSIS OF CARDIAC FUNCTION

## Nicolas Duchateau (1,2)

1. CREATIS lab, Université Lyon 1, France; 2. Institut Universitaire de France (IUF)

### 1. Introduction

It is crucial to go beyond routine clinical measurements to characterize the complexity of (ab)normal cardiac function. Recent advances in machine learning allow extracting and analyzing fine biomarkers from images. However, it is still challenging to analyze these high-dimensional and heterogeneous data across populations [1]. This presentation will overview our recent work on representation learning and multimodal information fusion to enrich both the extraction of physiologically relevant biomarkers, and to analyze these data across populations, with specific care to the complementarity of available biomarkers and physiological interpretation.

### 2. Materials and Methods

We focused on fine biomarkers of the cardiac function (myocardial shape and deformation, mostly), at each point of the myocardium and each instant of the cycle. We specifically improved existing segmentation models to provide physiologically/temporally consistent and generalizable results, with variational auto-encoders [2] and reinforcement learning [3]. Multi-channel representation learning was used for the population analysis of complex high-dimensional biomarkers, of heterogeneous types [4,5]. Hierarchical strategies (either imposed a-priori by Gaussian processes according to the complexity of the data [5], or learnt automatically by reinforcement learning [6]) improved the robustness of the learnt representations from more challenging data and defined optimal data integration strategies.

### 3. Results

We demonstrated the value of these tools on a broad range of applications involving routine cardial imaging, mostly 2D echocardiography and MRI. We reached anatomically- and temporally-consistent segmentations of

standard B-mode echocardiographic sequences. We currently extend this towards motion tracking using simulators of ultrasound image formation to generate ground truth data for training. We were able to represent complex diseases involving subtle differences across patients, among which lesion patterns in myocardial infarct and reperfusion injury, and shape/deformation changes across the continuum of hypertension grades.

### 4. Discussion and Conclusions

Representation learning is key to enrich both the quantification of physiologically-relevant biomarkers from cardiac images, and the statistical analysis of populations, paving the way for better understanding of complex diseases and corresponding risk stratification.

### 5. References

1. Duchateau et al. Front Cardiovasc Medicine 2020;6:190.

2. Painchaud et al. IEEE Trans Med Imaging 2022;41:2867-78.

3. Judge et al. Proc. MICCAI, LNCS 2024;15009:235-44.

4. Di Folco et al. Med Image Anal 2022;75:102278.

5. Freiche et al. IEEE Trans Med Imaging 2025;44:1529-40.

6. Bernardino et al. Proc. MICCAI, LNCS 2022:13431:592-601.

### Acknowledgements

The presenter acknowledges the valuable contributions from all collaborators and co-authors involved in the listed works. He also acknowledges the partial support from the French National Research Agency (ANR) through the LABEX PRIMES of Université de Lyon (ANR-11-LABX-0063) and the MIC-MAC project (ANR-19-CE45-0005), the Fédération Francaise de Cardiologie (MI-MIX project, Allocation René Foudon), the Université Claude Bernard Lyon 1 (AAP SENS 2022), and the Institut Universitaire de France.