@conference {cJimenezb, title = {Segmenting Invasive and In Situ Carcinoma in Breast WSIs with a Pretrained Histopathology Transformer}, booktitle = {2nd Deep Breast Workshop on AI and Imaging for Diagnostic and Treatment Challenges in breast care - MICCAI2025}, year = {In Press}, author = {Lauren Jimenez and Felip Vilardel and Ana Petit and Eduard Dorca and Teresa Soler-Mons{\'o} and Irma Ramos-Oliver and Laura Pons and Pedro L. Fern{\'a}ndez and Laia Adalid-Llansa and Daniel Mata and Lourdes Salazar-Huayna and Casas, J. and Ver{\'o}nica Vilaplana} } @conference {cPina25, title = {Cell Detection with Transformers {\textendash} A Paradigm Shift from Segmentation to Detection in Digital Pathology}, booktitle = {21st European Congress on Digital Pathology}, year = {2025}, month = {06/2025}, abstract = {

Cell nuclei detection and classification are fundamental tasks in digital pathology, enabling biomarker quantification and tumor microenvironment analysis. Although the clinically relevant objective is to detect and classify individual cells, these tasks are typically addressed using segmentation-based methods. While effective, segmentation introduces significant computational overhead. To overcome these limitations, we propose CellNuc-DETR, a transformer-based detection model that directly detects and classifies cells, offering a more efficient and scalable alternative to segmentation-based approaches

}, author = {Oscar Pina and Eduard Dorca and Ver{\'o}nica Vilaplana} } @conference {cPina25a, title = {Hierarchical Cell-to-Patch Graphs for Context-Aware Cell Classification in Digital Pathology}, booktitle = {21st European Congress on Digital Pathology}, year = {2025}, month = {06/2025}, publisher = {The European Society of Digital and Integrative Pathology}, organization = {The European Society of Digital and Integrative Pathology}, address = {Barcelona}, abstract = {

Cell classification in digital pathology requires spatial context beyond individual cell morphology, particularly for distinguishing tumor vs. normal cells in H\&E-stained images. While traditional segmentation and models like CellNuc-DETR achieve high detection accuracy, their reliance on small image patches limits classification performance by excluding tissue-level context. We propose a hierarchical cell-to-patch graph framework that integrates fine-grained cell features with global spatial information using graph neural networks (GNNs) to enhance classification accuracy while maintaining computational efficiency.

}, url = {https://www.ecdp2025.org/ecdp2025-program.pdf}, author = {Oscar Pina and Lauren Jimenez and Eduard Dorca and Ver{\'o}nica Vilaplana} } @conference {cDorca25, title = {Multimodal AI for Atypical Hyperplasia Diagnosis: A Comparative Study of CNNs, GNNs, and Hybrid Models}, booktitle = {21st European Congress on Digital Pathology}, year = {2025}, month = {06/2025}, publisher = {The European Society of Digital and Integrative Pathology}, organization = {The European Society of Digital and Integrative Pathology}, address = {Barcelona}, abstract = {

The diagnosis of atypical hyperplasia in endometrial aspirates is complex due to morphological variations across the menstrual cycle, often leading to interobserver variability. This study explores the use of artificial intelligence to enhance diagnostic accuracy by developing an AI-assisted framework to improve reproducibility and clinical decision-making in gynecological pathology.

}, url = {https://www.ecdp2025.org/ecdp2025-program.pdf}, author = {Eduard Dorca and Oscar Pina and Maria del Rosario Taco and Esther Guerra Fermamdez and Ver{\'o}nica Vilaplana and August Vidal Bel} } @conference {cPina25b, title = {Unsupervised Domain Adaptation for Cell Detection Across Histopathological Stains}, booktitle = {21st European Congress on Digital Pathology}, year = {2025}, month = {06/2025}, publisher = {The European Society of Digital and Integrative Pathology}, organization = {The European Society of Digital and Integrative Pathology}, address = {Barcelona}, abstract = {

Deep learning models trained on hematoxylin and eosin (H\&E) stained images often fail to generalize to immunohistochemistry (IHC) stains due to domain shifts. The scarcity of annotated IHC datasets limits the applicability of supervised learning. To address this challenge, we propose an unsupervised domain adaptation (UDA) approach that enables CellNuc-DETR to generalize from H\&E to IHC without requiring additional manual annotations.

}, url = {https://www.ecdp2025.org/ecdp2025-program.pdf}, author = {Oscar Pina and Eduard Dorca and Ver{\'o}nica Vilaplana} } @conference {cPinad, title = {Cell-DETR: Efficient cell detection and classification in WSIs with transformers}, booktitle = {Medical Imaging with Deep Learning (MIDL 2024)}, year = {2024}, month = {07/2024}, abstract = {

Understanding cell interactions and subpopulation distribution is crucial for pathologists to support their diagnoses. This cell information is traditionally extracted from segmentation methods, which poses significant computational challenges on processing Whole Slide Images (WSIs) due to their giga-size nature. Nonetheless, the clinically relevant tasks are nuclei detection and classification rather than segmentation. In this manuscript, we undertake a comprehensive exploration of the applicability of detection transformers for cell detection and classification (Cell-DETR). Not only do we demonstrate the effectiveness of the method by achieving state-of-the-art performance on well-established benchmarks, but we also develop a pipeline to tackle these tasks on WSIs at scale to enable the development of downstream applications. We show its efficiency and feasibility by reporting a significantly faster inference time on a dataset featuring large WSIs. By addressing the challenges associated with large-scale cell detection, our work contributes valuable insights that pave the way for improved diagnostics and downstream applications leveraging cell-level information.

}, author = {Oscar Pina and Eduard Dorca and Ver{\'o}nica Vilaplana} }