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

Sopa: a technology-invariant pipeline for analyses of image-based spatial omics
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
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
Structured State Space Models for Multiple Instance Learning in Digital Pathology

MICCAI 2023
Unsupervised Nuclei Segmentation using Spatial Organization Priors
Unsupervised Nuclei Segmentation using Spatial Organization Priors

MICCAI 2022
Region-guided CycleGANs for Stain Transfer in Whole Slide Images
Region-guided CycleGANs for Stain Transfer in Whole Slide Images

MICCAI 2022
Self-Supervised Representation Learning using Visual Field Expansion on Digital Pathology
Self-Supervised Representation Learning using Visual Field Expansion on Digital Pathology

ICCV CDpath Workshop 2021
Self-Supervised Nuclei Segmentation in Histopathological Images Using Attention
Self-Supervised Nuclei Segmentation in Histopathological Images Using Attention

MICCAI 2020