2 citations · 6 across the 6 of their papers we have counts for
6 papers
Single-Cell Cross-Modal Transfer by Adversarial Fine-Tuning of Foundation Models
Joseph Boyd, Matthew Lyon, Martino Mansoldo +2
Spatial transcriptomics (ST) is a powerful tool for exploring biological properties dependent on structure, proximity, and interaction in tissue. The methods underpinning ST are de…
Structured State Space Models for Multiple Instance Learning in Digital Pathology
Leo Fillioux, Joseph Boyd, Maria Vakalopoulou +2
Multiple instance learning is an ideal mode of analysis for histopathology data, where vast whole slide images are typically annotated with a single global label. In such cases, a…
Artifact Removal in Histopathology Images
Cameron Dahan, Stergios Christodoulidis, Maria Vakalopoulou +1
In the clinical setting of histopathology, whole-slide image (WSI) artifacts frequently arise, distorting regions of interest, and having a pernicious impact on WSI analysis. Image…
Region-guided CycleGANs for Stain Transfer in Whole Slide Images
Joseph Boyd, Irène Villa, Marie-Christine Mathieu +4
In whole slide imaging, commonly used staining techniques based on hematoxylin and eosin (H&E) and immunohistochemistry (IHC) stains accentuate different aspects of the tissue land…
Self-Supervised Representation Learning using Visual Field Expansion on Digital Pathology
Joseph Boyd, Mykola Liashuha, Eric Deutsch +3
The examination of histopathology images is considered to be the gold standard for the diagnosis and stratification of cancer patients. A key challenge in the analysis of such imag…
ASNI: Adaptive Structured Noise Injection for shallow and deep neural networks
Beyrem Khalfaoui, Joseph Boyd, Jean-Philippe Vert
Dropout is a regularisation technique in neural network training where unit activations are randomly set to zero with a given probability \emph{independently}. In this work, we pro…