Combining graph neural networks and computer vision methods for cell nuclei classification in lung tissue.

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In this article, we propose and analyse a novel approach that combines computer vision-based models with graph neural networks to improve the accuracy of automated tumoural cell detection in lung tissue. Our proposal leverages the inherent structure and relationships between cells in the tissue. Experimental results on our own curated dataset show that modelling the problem with graphs gives the model a clear advantage over just working at pixel level. This change in perspective provides extra information that makes it possible to improve the performance. The reduction of dimensionality that comes from working with the graph also allows us to increase the field of view with low computational requirements. Code is available at https://github.com/Jerry-Master/lung-tumour-study, models are uploaded to https://huggingface.co/Jerry-Master/Hovernet-plus-Graphs, and the dataset is published on Zenodo https://zenodo.org/doi/10.5281/zenodo.8368122.", "claims": [{"id": "claim-data-access", "statement": "For the sake of reproducibility we provide the source code used to obtain the results, which is available on GitHub, the pretrained models which are uploaded to Hugging Face and the dataset which is uploaded at Zenodo.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38590866", "locator": "sections[22] \"Conclusions\" paragraph 3", "quote": "For the sake of reproducibility we provide the source code used to obtain the results, which is available on GitHub, the pretrained models which are uploaded to Hugging Face and the dataset which is uploaded at Zenodo."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:38590866"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "blocked", "blocked_on": "data:accession_absent", "description": "The GEO series accession resolves and its sample accessions are listed. 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