@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} } @phdthesis {dPina25, title = {From pixels to graphs: cell-based digital pathology image analysis}, volume = {PhD}, year = {2025}, month = {06/2025}, type = {PhD Thesis}, abstract = {

Histopathology is the gold standard for cancer diagnosis, where pathologists examine tissue samples to assess cellular morphology, tissue architecture, and biomarker expression. The digitization of histological slides has enabled the application of artificial intelligence to assist in this process. However, deploying deep learning models in pathology remains challenging due to the gigapixel-scale nature of whole slide images, variability introduced by staining protocols and tissue morphology, and the scarcity of high-quality annotations. This thesis focuses on the development of deep learning methodologies for the identification and characterization of cells in histopathological slides, addressing these core challenges. Inspired by the way pathologists assess slides, the proposed methods target both individual cells and their spatial organization within the tissue. A transformer-based model, CellNuc-DETR, is introduced for efficient cell nuclei detection and classification, achieving state-of-the-art performance while significantly reducing inference time. To address annotation scarcity in immunohistochemistry, the model is adapted using unsupervised domain adaptation techniques, enabling knowledge transfer from hematoxylin and eosin-stained slides without requiring annotated immunohistochemistry data. To incorporate broader spatial context into cell classification, the thesis explores graph-based representations of histological tissue. It proposes a novel graph construction strategy that integrates both cell-level and patch-level nodes, enabling the fusion of cellular features with visual and contextual information in a scalable manner. To support annotation-efficient learning, the thesis introduces Regularized Graph Infomax, a self-supervised learning algorithm designed for representation learning on graphs without manual annotations. This algorithm is then applied to the proposed cell{\textendash}patch graphs to enable efficient, large-context cell classification with minimal supervision. Finally, the thesis demonstrates the clinical relevance of these methods in a study on endometrial carcinoma. A weakly supervised whole slide classifier is trained to predict molecular subtypes, and the developed cell detection and classification tools are used to extract cell-level biomarkers from predicted regions of interest. These biomarkers enable the quantification of subtype-specific cell spatial organization differences, linking computational predictions to interpretable biological features. By integrating transformers, graph-based representations, and self-supervised learning, this thesis introduces scalable and annotation-efficient methodologies for computational pathology. These approaches enhance the efficiency, robustness, and generalization of artificial intelligence models, contributing to the development of clinically relevant tools for cancer diagnosis and characterization.

}, doi = {10.5821/dissertation-2117-439958}, url = {http://hdl.handle.net/10803/695086}, author = {Oscar Pina}, editor = {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} } @article {aPinaa, title = {Layer-Wise Training of Graph Neural Networks With Self-Supervised Learning}, journal = { IEEE Transactions on Neural Networks and Learning Systems,}, year = {2025}, month = {07/2025}, doi = {10.1109/TNNLS.2025.3577702}, author = {Oscar Pina 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} } @article {aPerez-Cano24, title = {Combining graph neural networks and computer vision methods for cell nuclei classification in lung tissue}, journal = {Heliyon}, volume = {10}, year = {2024}, month = {03/2024}, issn = {2405-8440}, doi = {https://doi.org/10.1016/j.heliyon.2024.e28463}, author = {Jose Perez-Cano and Irene Sansano Valero and D. Anglada and Oscar Pina and Salembier, P. and Marqu{\'e}s, F.} } @article {aPina, title = {Feature propagation as self-supervision signals on graphs}, journal = {Knowledge-Based Systems}, volume = {289}, year = {2024}, month = {04/2024}, doi = {https://doi.org/10.1016/j.knosys.2024.111512}, author = {Oscar Pina and Ver{\'o}nica Vilaplana} } @conference {cPinac, title = {Unsupervised Domain Adaptation for Multi-Stain Cell Detection in Breast Cancer with Transformers}, booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (DEF-AI-MIA workshop)}, year = {2024}, month = {07/2024}, abstract = {

The complexity in digital pathology image analysis arises from the inherent variability in histopathological slides including tissue specimen differences and stain variations. While publicly available datasets are increasing they primarily focus on hematoxylin and eosin (H\&E) staining yet pathologists often require analysis across multiple stains for comprehensive diagnosis. The poor cross-domain generalization of deep learning models intensifies these challenges making it unfeasible to implement without exhaustive annotations in each stain. This manual curation process is time-consuming and requires the creation of sufficiently large and precise datasets. In this work we address these challenges focusing on breast cancer across four crucial stains: ER PR HER2 and Ki-67. Given the necessity of cell-level information for diagnosis we concentrate on cell detection tasks. Leveraging unsupervised domain adaptation techniques we bridge the gap between publicly available annotated H\&E datasets and unlabeled data in other stains. Utilizing detection transformers we demonstrate the superiority of adversarial feature learning over source-only and image-level generative methods. Our work contributes to improving digital pathology analysis by enabling robust and efficient pipelines across multiple stains. By leveraging unsupervised domain adaptation we facilitate the development of accurate computer-aided diagnosis pipelines capable of handling stain variations and enhancing diagnostic accuracy in practical settings.

}, url = {https://openaccess.thecvf.com/content/CVPR2024W/DEF-AI-MIA/papers/Pina_Unsupervised_Domain_Adaptation_for_Multi-Stain_Cell_Detection_in_Breast_Cancer_CVPRW_2024_paper.pdf}, author = {Oscar Pina and Ver{\'o}nica Vilaplana} } @conference {cPinab, title = {Layer-wise self-supervised learning on graphs}, booktitle = {KDD 2023 Workshop on Deep Learning on Graphs: Methods and Applications (DLG-KDD 2023)}, year = {2023}, month = {08/2023}, address = {Long Beach, USA}, abstract = {

End-to-end training of graph neural networks (GNN) on large graphs presents several memory and computational challenges, and limits the application to shallow architectures as depth exponentially increases the memory and space complexities. In this manuscript, we propose Layer-wise Regularized Graph Infomax, an algorithm to train GNNs layer by layer in a self-supervised manner. We decouple the feature propagation and feature transformation carried out by GNNs to learn node representations in order to derive a loss function based on the prediction of future inputs. We evaluate the algorithm in inductive large graphs and show similar performance to other end to end methods and a substantially increased efficiency, which enables the training of more sophisticated models in one single device. We also show that our algorithm avoids the oversmoothing of the representations, another common challenge of deep GNNs.

}, author = {Oscar Pina and Ver{\'o}nica Vilaplana} } @conference {cPinaa, title = {Self-supervised graph representations of WSIs}, booktitle = {Geometric Deep Learning in Medical Image Analysis}, year = {2022}, month = {2022}, abstract = {

In this manuscript we propose a framework for the analysis of whole slide images (WSI) on the cell entity space with self-supervised deep learning on graphs and explore its representation quality at different levels of application. It consists of a two step process in which the cell level analysis is performed locally, by clusters of nearby cells that can be seen as small regions of the image, in order to learn representations that capture the cell environment and distribution. In a second stage, a WSI graph is generated with these regions as nodes and the representations learned as initial node embeddings. The graph is leveraged for a downstream task, region of interest (ROI) detection addressed as a graph clustering. The representations outperform the evaluation baselines at both levels of application, which has been carried out predicting whether a cell, or region, is tumor or not based on its learned representations with a logistic regressor.

}, url = {https://proceedings.mlr.press/v194/pina22a/pina22a.pdf}, author = {Oscar Pina and Ver{\'o}nica Vilaplana} } @conference {cPina, title = {Structural Networks for Brain Age Prediction}, booktitle = {Medical Imaging with Deep Learning (MIDL 2022)}, year = {2022}, month = {08/2022}, abstract = {

Biological networks have gained considerable attention within the Deep Learning community because of the promising framework of Graph Neural Networks (GNN), neural models that operate in complex networks. In the context of neuroimaging, GNNs have successfully been employed for functional MRI processing but their application to ROI-level structural MRI (sMRI) remains mostly unexplored. In this work we analyze the implementation of these geometric models with sMRI by building graphs of ROIs (ROI graphs) using tools from Graph Signal Processing literature and evaluate their performance in a downstream supervised task, age prediction. We first make a qualitative and quantitative comparison of the resulting networks obtained with common graph topology learning strategies. In a second stage, we train GNN-based models for brain age prediction. Since the order of every ROI graph is exactly the same and each vertex is an entity by itself (a ROI), we evaluate whether including ROI information during message-passing or global pooling operations is beneficial and compare the performance of GNNs against a Fully-Connected Neural Network baseline. The results show that ROI-level information is needed during the global pooling operation in order to achieve competitive results. However, no relevant improvement has been detected when it is incorporated during the message passing. These models achieve a MAE of 4.27 in hold-out test data, which is a performance very similar to the baseline, suggesting that the inductive bias included with the obtained graph connectivity is relevant and useful to reduce the dimensionality of the problem

}, author = {Oscar Pina and Irene Cumplido-Mayoral and Raffaele Cacciaglia and Jos{\'e} Mar{\'\i}a Gonz{\'a}lez-de-Ech{\'a}varri and Juan D. Gispert and Ver{\'o}nica Vilaplana} }