We are part of the School of Computer Science (IC) and the School of Life-Sciences (SV) at EPFL in Lausanne, Switzerland. The group is led by Charlotte Bunne. Our research aims to advance personalized medicine by utilizing machine learning and large-scale biomedical data.
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A standardized benchmarking framework for evaluating spatial proteomics foundation models across subcellular, cellular and tissue scales, enabling systematic assessment of virtual staining, cell se…
Python 8
A multimodal foundation model for joint analysis of histopathology and spatial proteomics data, enabling cross-modal virtual staining, cellular phenotyping, biomarker discovery and clinically rele…
Python 5
A spatial proteomics foundation model for analyzing multiplexed tissue imaging data across molecular, cellular and tissue scales enabling clinical diagnostics, biological discovery and patient case retrieval.
A unified data loading and processing framework for harmonized spatial proteomics and histopathology datasets across imaging technologies, enabling efficient access, standardization and sampling of multimodal tissue imaging data at scale.
A multimodal foundation model for joint analysis of histopathology and spatial proteomics data, enabling cross-modal virtual staining, cellular phenotyping, biomarker discovery and clinically relevant patient stratification across diverse tissues and imaging technologies.
A standardized benchmarking framework for evaluating spatial proteomics foundation models across subcellular, cellular and tissue scales, enabling systematic assessment of virtual staining, cell segmentation, phenotyping and clinically relevant tissue-level predictions.
A task-agnostic, self-supervised framework for sequential modality acquisition in multimodal foundation models, enabling cost-efficient inference through informative measurement selection without requiring downstream task knowledge or labels.
A benchmark for evaluating multimodal large language models in complex oncology decision-making, assessing multimodal integration across clinical data sources and longitudinal reasoning over patient timelines.
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