Pan-tumor T-lymphocyte detection using deep neural networks: Recommendations for transfer learning in immunohistochemistry

Wilm, Frauke; Ihling, Christian; Méhes, Gábor; Terracciano, Luigi; Puget, Chloé; Klopfleisch, Robert; Schüffler, Peter J.; Aubreville, Marc; Maier, Andreas; Mrowiec, Thomas; Breininger, Katharina

doi:10.1016/j.jpi.2023.100301

Cited by 2 publications

(2 citation statements)

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“…The locality sensitive model (LSM) introduces restrictions towards sparsity in nucleus center, which has been used for CD8+ lymphocyte detection [27]. [28] established a detector for CD3+ cells, tumor cells, and other cells based on RetinaNet, which was extensively validated in head and neck cancer, lung cancer, breast cancer, and gastric cancer. Nevertheless, challenges from stain artifacts, tissue folds, and dense regions are persisted.…”

Section: B Ihc Scoringmentioning

confidence: 99%

“…Pan-cancer CD3: It provides 92 regions of interest from slides stained with CD3, each measuring 2mm 2 , which involved head and neck squamous cell carcinoma, non-small cell lung cancer, triple-negative breast cancer, and gastric cancer [28]. The image resolution was 0.23μm/px, and the cellular annotation was performed jointly by pathologists and semi-automatic commercial software.…”

Section: Shidc-b-ki-67mentioning

confidence: 99%

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LYSTO: The Lymphocyte Assessment Hackathon and Benchmark Dataset

Jiao,

van der Laak,

Albarqouni

et al. 2024

IEEE J. Biomed. Health Inform.

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We introduce LYSTO, the Lymphocyte Assessment Hackathon, which was held in conjunction with the MICCAI 2019 Conference in Shenzhen (China). The competition required participants to automatically assess the number of lymphocytes, in particular T-cells, in images of colon, breast, and prostate cancer stained with CD3 and CD8 immunohistochemistry. Differently from other challenges setup in medical image analysis, LYSTO participants were solely given a few hours to address this problem. In this paper, we describe the goal and the multi-phase organization of the hackathon; we describe the proposed methods and the on-site results. Additionally, we present post-competition results where we show how the Manuscript

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Section: B Ihc Scoringmentioning

confidence: 99%

Section: Shidc-b-ki-67mentioning

confidence: 99%