11 papers · 1 filter
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…
Repurposing Language Models into Embedding Models: Finding the Compute-Optimal Recipe
Alicja Ziarko, Albert Q. Jiang, Bartosz Piotrowski +3
Text embeddings are essential for many tasks, such as document retrieval, clustering, and semantic similarity assessment. In this paper, we study how to contrastively train text em…
TabEBM: A Tabular Data Augmentation Method with Distinct Class-Specific Energy-Based Models
Andrei Margeloiu, Xiangjian Jiang, Nikola Simidjievski +1
Data collection is often difficult in critical fields such as medicine, physics, and chemistry. As a result, classification methods usually perform poorly with these small datasets…
End-to-End Ontology Learning with Large Language Models
Andy Lo, Albert Q. Jiang, Wenda Li +1
Ontologies are useful for automatic machine processing of domain knowledge as they represent it in a structured format. Yet, constructing ontologies requires substantial manual eff…
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…
Efficient Bias Mitigation Without Privileged Information
Mateo Espinosa Zarlenga, Swami Sankaranarayanan, Jerone T. A. Andrews +3
Deep neural networks trained via empirical risk minimisation often exhibit significant performance disparities across groups, particularly when group and task labels are spuriously…