activity
20182024
most citedTowards Explainable Graph Representations in Digital Pathology

31 citations · 93 across the 15 of their papers we have counts for

collaborators

16 papers

quant-ph2024★ 13 cited

Efficient Parameter Optimisation for Quantum Kernel Alignment: A Sub-sampling Approach in Variational Training

M. Emre Sahin, Benjamin C. B. Symons, Pushpak Pati +5

Quantum machine learning with quantum kernels for classification problems is a growing area of research. Recently, quantum kernel alignment techniques that parameterise the kernel…

eess.IV2023★ 1 cited

Weakly Supervised Joint Whole-Slide Segmentation and Classification in Prostate Cancer

Pushpak Pati, Guillaume Jaume, Zeineb Ayadi +4

The segmentation and automatic identification of histological regions of diagnostic interest offer a valuable aid to pathologists. However, segmentation methods are hampered by the…

cs.CV2022★ 3 cited

Differentiable Zooming for Multiple Instance Learning on Whole-Slide Images

Kevin Thandiackal, Boqi Chen, Pushpak Pati +4

Multiple Instance Learning (MIL) methods have become increasingly popular for classifying giga-pixel sized Whole-Slide Images (WSIs) in digital pathology. Most MIL methods operate…

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★ 3 cited

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…