6 citations · 20 across the 8 of their papers we have counts for
8 papers
Towards Spatial Transcriptomics-driven Pathology Foundation Models
Konstantin Hemker, Andrew H. Song, Cristina Almagro-Pérez +6
Spatial transcriptomics (ST) provides spatially resolved measurements of gene expression, enabling characterization of the molecular landscape of human tissue beyond histological a…
AI-driven 3D Spatial Transcriptomics
Cristina Almagro-Pérez, Andrew H. Song, Luca Weishaupt +13
A comprehensive three-dimensional (3D) map of tissue architecture and gene expression is crucial for illuminating the complexity and heterogeneity of tissues across diverse biomedi…
Measuring Cross-Modal Interactions in Multimodal Models
Laura Wenderoth, Konstantin Hemker, Nikola Simidjievski +1
Integrating AI in healthcare can greatly improve patient care and system efficiency. However, the lack of explainability in AI systems (XAI) hinders their clinical adoption, especi…
PATHS: A Hierarchical Transformer for Efficient Whole Slide Image Analysis
Zak Buzzard, Konstantin Hemker, Nikola Simidjievski +1
Computational analysis of whole slide images (WSIs) has seen significant research progress in recent years, with applications ranging across important diagnostic and prognostic tas…
Multimodal Lego: Model Merging and Fine-Tuning Across Topologies and Modalities in Biomedicine
Konstantin Hemker, Nikola Simidjievski, Mateja Jamnik
Learning holistic computational representations in physical, chemical or biological systems requires the ability to process information from different distributions and modalities…
HEALNet: Multimodal Fusion for Heterogeneous Biomedical Data
Konstantin Hemker, Nikola Simidjievski, Mateja Jamnik
Technological advances in medical data collection, such as high-throughput genomic sequencing and digital high-resolution histopathology, have contributed to the rising requirement…
CGXplain: Rule-Based Deep Neural Network Explanations Using Dual Linear Programs
Konstantin Hemker, Zohreh Shams, Mateja Jamnik
Rule-based surrogate models are an effective and interpretable way to approximate a Deep Neural Network's (DNN) decision boundaries, allowing humans to easily understand deep learn…
Feature Synergy, Redundancy, and Independence in Global Model Explanations using SHAP Vector Decomposition
Jan Ittner, Lukasz Bolikowski, Konstantin Hemker +1
We offer a new formalism for global explanations of pairwise feature dependencies and interactions in supervised models. Building upon SHAP values and SHAP interaction values, our…