Computational Pathology
Computational Pathology has revolutionized histopathological analysis by leveraging sophisticated computational techniques to augment clinical workflows. Our team is implicated in multiple projects aiming to develop novel methods for processing gigapixel sized digital pathology slides for variety of cancer types. Within this framework we have proposed a number of self-supervised tasks for cell/nuclei segmentation, stain tranfer or WSI-level prediction endpoints.
Related Publications
THUNDER: Tile-level Histopathology image UNDERstanding benchmark
NeurIPS 2025 Datasets and Benchmarks Track
HistAug : Controllable Latent-Space Augmentation for Digital Pathology
ICCV 2025
Sopa: a technology-invariant pipeline for analyses of image-based spatial omics
Nature Communications 2024
Trastuzumab Deruxtecan in Metastatic Breast Cancer with Variable HER2 Expression: the Phase 2 DAISY Trial
Nature Medicine 2023
Structured State Space Models for Multiple Instance Learning in Digital Pathology
MICCAI 2023
Unsupervised Nuclei Segmentation using Spatial Organization Priors
MICCAI 2022
Region-guided CycleGANs for Stain Transfer in Whole Slide Images
MICCAI 2022
Self-Supervised Representation Learning using Visual Field Expansion on Digital Pathology
ICCV CDpath Workshop 2021
Self-Supervised Nuclei Segmentation in Histopathological Images Using Attention
MICCAI 2020