58 citations · 59 across the 6 of their papers we have counts for
6 papers
DALPHIN: Benchmarking Digital Pathology AI Copilots Against Pathologists on an Open Multicentric Dataset
Carlijn Lems, Sander Moonemans, Natálie Klubíčková +53
Foundation models with visual question answering capabilities for digital pathology are emerging. Such unprecedented technology requires independent benchmarking to assess its pote…
Leveraging Adversarial Learning for Pathological Fidelity in Virtual Staining
José Teixeira, Pascal Klöckner, Diana Montezuma +5
In addition to evaluating tumor morphology using H&E staining, immunohistochemistry is used to assess the presence of specific proteins within the tissue. However, this is a costly…
GANs vs. Diffusion Models for virtual staining with the HER2match dataset
Pascal Klöckner, José Teixeira, Diana Montezuma +3
Virtual staining is a promising technique that uses deep generative models to recreate histological stains, providing a faster and more cost-effective alternative to traditional ti…
CountPath: Automating Fragment Counting in Digital Pathology
Ana Beatriz Vieira, Maria Valente, Diana Montezuma +8
Quality control of medical images is a critical component of digital pathology, ensuring that diagnostic images meet required standards. A pre-analytical task within this process i…
A Survey on Cell Nuclei Instance Segmentation and Classification: Leveraging Context and Attention
João D. Nunes, Diana Montezuma, Domingos Oliveira +2
Manually annotating nuclei from the gigapixel Hematoxylin and Eosin (H&E)-stained Whole Slide Images (WSIs) is a laborious and costly task, meaning automated algorithms for cell nu…
An interpretable machine learning system for colorectal cancer diagnosis from pathology slides
Pedro C. Neto, Diana Montezuma, Sara P. Oliveira +10
Considering the profound transformation affecting pathology practice, we aimed to develop a scalable artificial intelligence (AI) system to diagnose colorectal cancer from whole-sl…