@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 {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} }