31 citations · 48 across the 6 of their papers we have counts for
7 papers
Hierarchical Graph Representations in Digital Pathology
Pushpak Pati, Guillaume Jaume, Antonio Foncubierta +14
Cancer diagnosis, prognosis, and therapy response predictions from tissue specimens highly depend on the phenotype and topological distribution of constituting histological entitie…
Learning Whole-Slide Segmentation from Inexact and Incomplete Labels using Tissue Graphs
Valentin Anklin, Pushpak Pati, Guillaume Jaume +6
Segmenting histology images into diagnostically relevant regions is imperative to support timely and reliable decisions by pathologists. To this end, computer-aided techniques have…
Marginal loss and exclusion loss for partially supervised multi-organ segmentation
Gonglei Shi, Li Xiao, Yang Chen +1
Annotating multiple organs in medical images is both costly and time-consuming; therefore, existing multi-organ datasets with labels are often low in sample size and mostly partial…
Mitosis Detection Under Limited Annotation: A Joint Learning Approach
Pushpak Pati, Antonio Foncubierta-Rodriguez, Orcun Goksel +1
Mitotic counting is a vital prognostic marker of tumor proliferation in breast cancer. Deep learning-based mitotic detection is on par with pathologists, but it requires large labe…
HACT-Net: A Hierarchical Cell-to-Tissue Graph Neural Network for Histopathological Image Classification
Pushpak Pati, Guillaume Jaume, Lauren Alisha Fernandes +13
Cancer diagnosis, prognosis, and therapeutic response prediction are heavily influenced by the relationship between the histopathological structures and the function of the tissue.…
Towards Explainable Graph Representations in Digital Pathology
Guillaume Jaume, Pushpak Pati, Antonio Foncubierta-Rodriguez +6
Explainability of machine learning (ML) techniques in digital pathology (DP) is of great significance to facilitate their wide adoption in clinics. Recently, graph techniques encod…