Cell-DETR: Efficient cell detection and classification in WSIs with transformers

Oscar Pina, Eduard Dorca, Veronica Vilaplana
Proceedings of The 7nd International Conference on Medical Imaging with Deep Learning, PMLR 250:1128-1141, 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 x3.4 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 paves the way for the development of scalable diagnosis pipelines based on cell-level information.

Cite this Paper

BibTeX
@InProceedings{pmlr-v250-pina24a, title = {Cell-DETR: Efficient cell detection and classification in WSIs with transformers}, author = {Pina, Oscar and Dorca, Eduard and Vilaplana, Veronica}, booktitle = {Proceedings of The 7nd International Conference on Medical Imaging with Deep Learning}, pages = {1128--1141}, year = {2024}, editor = {Burgos, Ninon and Petitjean, Caroline and Vakalopoulou, Maria and Christodoulidis, Stergios and Coupe, Pierrick and Delingette, Hervé and Lartizien, Carole and Mateus, Diana}, volume = {250}, series = {Proceedings of Machine Learning Research}, month = {03--05 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v250/main/assets/pina24a/pina24a.pdf}, url = {https://proceedings.mlr.press/v250/pina24a.html}, 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 x3.4 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 paves the way for the development of scalable diagnosis pipelines based on cell-level information.} }
Endnote
%0 Conference Paper %T Cell-DETR: Efficient cell detection and classification in WSIs with transformers %A Oscar Pina %A Eduard Dorca %A Veronica Vilaplana %B Proceedings of The 7nd International Conference on Medical Imaging with Deep Learning %C Proceedings of Machine Learning Research %D 2024 %E Ninon Burgos %E Caroline Petitjean %E Maria Vakalopoulou %E Stergios Christodoulidis %E Pierrick Coupe %E Hervé Delingette %E Carole Lartizien %E Diana Mateus %F pmlr-v250-pina24a %I PMLR %P 1128--1141 %U https://proceedings.mlr.press/v250/pina24a.html %V 250 %X 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 x3.4 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 paves the way for the development of scalable diagnosis pipelines based on cell-level information.
APA
Pina, O., Dorca, E. & Vilaplana, V.. (2024). Cell-DETR: Efficient cell detection and classification in WSIs with transformers. Proceedings of The 7nd International Conference on Medical Imaging with Deep Learning, in Proceedings of Machine Learning Research 250:1128-1141 Available from https://proceedings.mlr.press/v250/pina24a.html.

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