Conference Agenda

X-ray dark-field radiography provides complementary diagnostic information to conventional attenuation imaging by visualizing microstructural tissue changes through small-angle scattering. Early clinical studies suggest that dark-field chest radiography offers unique diagnostic value for quantifying pulmonary emphysema in COPD patients and improving COVID-19 diagnosis. While dark-field scanners provide paired attenuation and dark-field radiographs, the limited availability of such data poses challenges for developing robust deep learning models.

In our recent work [1], we present the first framework for generating dark-field images directly from conventional 2D chest X-ray radiographs using an Uncertainty-Guided Progressive Generative Adversarial Network. This approach enables the large-scale generation of virtual dark-field data from widely available chest X-rays.

The proposed framework follows a progressive learning scheme in which aleatoric uncertainty estimates are used as attention maps to guide model refinement across stages. We explicitly incorporate both aleatoric and epistemic uncertainty to improve interpretability and reliability, as they reflect different aspects of model confidence. High aleatoric uncertainty marks regions with inherently ambiguous signals, while elevated epistemic uncertainty highlights areas where the model may not generalize well. Together, these uncertainty estimates provide a more comprehensive view of model reliability and data quality.

Our results demonstrate that the proposed progressive model can accurately reconstruct dark-field images from attenuation data, achieving high image fidelity and structural consistency. Quantitatively, all metrics (MSE, PSNR, and SSIM) show consistent improvement across the model stages, confirming that progressive refinement effectively enhances fine structural detail and reduces artifacts. The final stage achieves the best overall performance, indicating that the model learns to recover increasingly realistic and anatomically consistent dark-field representations.

Furthermore, out-of-distribution evaluation demonstrates that the proposed model generalizes well and provides a promising foundation for future clinical applications.

[1] Lina Felsner, Henriette Bast, Tina Dorosti, Florian Schaff, Franz Pfeiffer, Daniela Pfeiffer, Julia Schnabel, ‘Uncertainty-guided Generation of Dark-field Radiographs’, IEEE International Symposium on Biomedical Imaging (ISBI) 2026

Ongoing environmental change in polar oceans is rapidly affecting phytoplankton communities living inside and below the sea ice. Assessing these changes in biodiversity requires determining species composition, as well as measuring physiological traits such as silicification and mixotrophy at a single-cell level. However, current methods are time-intensive, require expert training, and are subject to human bias. This project explores the use of AI in conjunction with multi-spectral imaging flow cytometry (MIFC) to develop a high-throughput methodology to assess biodiversity, silicification, and mixotrophy in polar phytoplankton. MIFC produces large datasets of multi-channel images capturing cell morphology, pigmentation, and fluorescence signals associated with physiological processes, providing a growing dataset of polar phytoplankton cells as a basis for automated taxonomic and functional classification.

However, several challenges complicate the application of AI methods to plankton images. These include: limited expert-labelled training data, high morphological variability within species, and class imbalance across taxa. This project is working in conjunction with other plankton imaging initiatives such as AqQua and the Helmholtz UNLOCK project AIMBIS, to build a curated dataset of polar phytoplankton images collected during laboratory experiments and recent Arctic and Antarctic expeditions which will be used to investigate CNN approaches for classifying taxa and functional traits such as silicification and mixotrophic behaviour.

By integrating imaging flow cytometry with machine learning approaches, this work aims to enable rapid identification of phytoplankton taxa and quantification of functional biodiversity traits such as silicification intensity and trophic modes whilst contributing to the development of automated and scalable tools for marine biodiversity monitoring.

The advent of Diffusion Transformers (DiT), exemplified by Sora, has revolutionized video generation by capturing complex spatiotemporal dependencies. However, their potential in 3D medical imaging remains largely underexplored. In this paper, we present a novel approach to unconditioned CT volume generation by adapting the Open-Sora architecture. Treating axial CT slices as temporal frames, we leverage the spatiotemporal attention mechanisms of video diffusion models to learn the high-dimensional distribution of anatomical structures without relying on explicit conditions (e.g., text prompts or segmentation maps). Our method addresses the challenges of volumetric consistency and data scarcity in medical domains. Extensive experiments demonstrate that our model generates high-fidelity, diverse 3D CT volumes that preserve structural integrity and tissue texture. Quantitative metrics and qualitative assessments by radiologists confirm the realism of the synthetic data. This work highlights the feasibility of repurposing state-of-the-art video generation models for 3D medical synthesis, paving the way for privacy-preserving data augmentation and foundational anatomy modeling. Furthermore, this model serves as a foundational prior that can be adapted for text-conditioned or image-conditioned generation tasks.

Tissue clearing combined with light-sheet microscopy (LSM) enables the 3D visualization and analysis of intricate cellular and subcellular structures across tissues and organisms, characterized by high contrast and super-resolution capabilities. However, segmenting diverse biological structures in 3D LSM images remains a major challenge due to significant variations in morphology, artifacts, signal-to-noise ratio, and surrounding tissue context. Conventional supervised learning-based segmentation models typically require extensive voxel-wise annotations and the training structure-specific models, thereby limiting their generalizability and scalability across datasets and applications.

Foundation models (FMs) are expected to mitigate this limitation. FMs trained on large-scale data are anticipated to achieve zero-shot or few-shot generalization. Despite their success in other domains, their application to LSM data remains underexplored. In this study, we propose a foundation model tailored for comprehensive segmentation of diverse biological structures in tissue-cleared mouse LSM dataset and evaluate its domain generalization capability. We construct a large-scale dataset containing more than 9 biological structures and 50,000 patches of size 300³ and perform self-supervised learning (SSL) to learn robust representations from diverse LSM data. The model is evaluated across extensive 3D segmentation tasks, including out-of-distribution datasets. Our findings demonstrate that a self-supervised pretrained foundation model enables effective cross-structure transfer for 3D image segmentation and indicate its strong ability to generalize to previously unseen biological structures. In several downstream tasks, it outperforms state-of-the-art task-specific 3D segmentation models.

Overall, this study underscores the potential of FMs in LSM image domain and demonstrates their capability as a unified approach for segmentation across diverse biological domains.

Factor analysis methods for multimodal data integration are widely used in medical applications. These methods aim to identify common latent factors underlying an observation for which multiple measurements are available, in an unsupervised manner - without prior knowledge of what these factors might correspond to, such as clinical features such as disease subtypes. Inference typically seeks to disentangle factors by enforcing orthogonality or sparsity constraints, or proceeds without imposing any specific structural assumptions. In (semi-)supervised settings, factor analysis models additionally focus on capturing associations between factors and covariates.

In this work, we focus on multimodal factor analysis and explore different ways of modelling factor dependencies - both internal relationships between factors and their relationships with additional covariates. We present a unified framework that describes these approaches from a new perspective, providing both mathematical insights and illustrative examples.