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Guillaume Jaume

7 accepted papers

2024

HEST-1k: A Dataset For Spatial Transcriptomics and Histology Image Analysis

Guillaume Jaume, Paul Doucet, Andrew H. Song, Ming Y. Lu, Cristina Almagro Pérez, Sophia J Wagner +5
NeurIPS 2024spotlight

Spatial transcriptomics enables interrogating the molecular composition of tissue with ever-increasing resolution and sensitivity. However, costs, rapidly evolving technology, and lack of standards have constrained computational methods in ST to narrow tasks and small cohorts. In addition, the under…

Cited by 33Source ↗PDF ↗code ↗Scholar ↗
2024

Modeling Dense Multimodal Interactions Between Biological Pathways and Histology for Survival Prediction

Guillaume Jaume, Anurag Vaidya, Richard J. Chen, Drew F.K. Williamson, Paul Pu Liang, Faisal Mahmood
CVPR 2024poster

Integrating whole-slide images (WSIs) and bulk transcriptomics for predicting patient survival can improve our understanding of patient prognosis. However this multimodal task is particularly challenging due to the different nature of these data: WSIs represent a very high-dimensional spatial descri…

Cited by 61Source ↗PDF ↗code ↗Scholar ↗
2024

Morphological Prototyping for Unsupervised Slide Representation Learning in Computational Pathology

Andrew H. Song, Richard J. Chen, Tong Ding, Drew F.K. Williamson, Guillaume Jaume, Faisal Mahmood
CVPR 2024poster

Representation learning of pathology whole-slide images (WSIs) has been has primarily relied on weak supervision with Multiple Instance Learning (MIL). However the slide representations resulting from this approach are highly tailored to specific clinical tasks which limits their expressivity and ge…

Cited by 38Source ↗PDF ↗code ↗Scholar ↗
2024

Multimodal Prototyping for cancer survival prediction

Andrew H. Song, Richard J. Chen, Guillaume Jaume, Anurag Jayant Vaidya, Alexander Baras, Faisal Mahmood
ICML 2024poster

Multimodal survival methods combining gigapixel histology whole-slide images (WSIs) and transcriptomic profiles are particularly promising for patient prognostication and stratification. Current approaches involve tokenizing the WSIs into smaller patches ($>10^4$ patches) and transcriptomics into ge…

Cited by 17Source ↗PDF ↗code ↗Scholar ↗
2024

Transcriptomics-guided Slide Representation Learning in Computational Pathology

Guillaume Jaume, Lukas Oldenburg, Anurag Vaidya, Richard J. Chen, Drew F.K. Williamson, Thomas Peeters +2
CVPR 2024poster

Self-supervised learning (SSL) has been successful in building patch embeddings of small histology images (e.g. 224 x 224 pixels) but scaling these models to learn slide embeddings from the entirety of giga-pixel whole-slide images (WSIs) remains challenging. Here we leverage complementary informati…

Cited by 30Source ↗PDF ↗code ↗Scholar ↗
2022

Differentiable Zooming for Multiple Instance Learning on Whole-Slide Images

Kevin Thandiackal, Boqi Chen, Pushpak Pati, Guillaume Jaume, Drew F. K. Williamson, Maria Gabrani +1
ECCV 2022poster

"Multiple Instance Learning (MIL) methods have become increasingly popular for classifying gigapixel-sized Whole-Slide Images (WSIs) in digital pathology. Most MIL methods operate at a single WSI magnification, by processing all the tissue patches. Such a formulation induces high computational requi…

Cited by 51Source ↗PDF ↗code ↗Scholar ↗
2021

Quantifying Explainers of Graph Neural Networks in Computational Pathology

Guillaume Jaume, Pushpak Pati, Behzad Bozorgtabar, Antonio Foncubierta, Anna Maria Anniciello, Florinda Feroce +4
CVPR 2021poster

Explainability of deep learning methods is imperative to facilitate their clinical adoption in digital pathology. However, popular deep learning methods and explainability techniques (explainers) based on pixel-wise processing disregard biological entities' notion, thus complicating comprehension by…

Cited by 107PDF ↗code ↗Scholar ↗