activity
20182024
most citedTowards Explainable Graph Representations in Digital Pathology

31 citations · 76 across the 11 of their papers we have counts for

collaborators
Showing 2021Show all

5 papers · 1 filter

q-bio.QM2021

BRACS: A Dataset for BReAst Carcinoma Subtyping in H&E Histology Images

Nadia Brancati, Anna Maria Anniciello, Pushpak Pati +10

Breast cancer is the most commonly diagnosed cancer and registers the highest number of deaths for women with cancer. Recent advancements in diagnostic activities combined with lar…

eess.IV2021★ 10 cited

HistoCartography: A Toolkit for Graph Analytics in Digital Pathology

Guillaume Jaume, Pushpak Pati, Valentin Anklin +2

Advances in entity-graph based analysis of histopathology images have brought in a new paradigm to describe tissue composition, and learn the tissue structure-to-function relations…

cs.CV2021

Generative Feature-driven Image Replay for Continual Learning

Kevin Thandiackal, Tiziano Portenier, Andrea Giovannini +2

Neural networks are prone to catastrophic forgetting when trained incrementally on different tasks. Popular incremental learning methods mitigate such forgetting by retaining a sub…

cs.CV2021

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…

cs.CV2021★ 1 cited

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…