biostudies-literatureSancere LDeutsche ForschungsgemeinschaftMinisterium für Kultur und Wissenschaft des Landes Nordrhein-WestfalenMinistry of Culture and Science of the State of Northrhine WestphaliaDeutsche Krebshilfee1013907https://www.ebi.ac.uk/biostudies/studies/S-EPMC12854473biostudies-literatureUnknown22(1)Recent advances in digital pathology have enabled comprehensive analyses of Whole-Slide Images (WSIs) from tissue samples, leveraging high-resolution microscopy and computational capabilities. Despite this progress, available tools for automatic cell type identification perform poorly on skin tissue, e.g. in the classification of non-melanoma tumor cells. This is due to a paucity of labeled training data sets and high morphological similarities between tumor and non-tumor epithelial cells in the skin. Here, we propose Histo-Miner, a deep learning-based pipeline designed for the analysis of skin WSIs. To this end we generated two new datasets using WSIs of cutaneous Squamous Cell Carcinoma (cSCC) samples, a frequent non-melanoma skin cancer, by annotating 47,392 cell nuclei across 5 cell tyPLoS computational biologyHisto-Miner: Deep learning based tissue features extraction pipeline from H&E whole slide images of cutaneous squamous cell carcinoma.PMC12854473311-8.03.03.02-1476357011330770116929CRC1399, project ID 413326622CRC1310, project ID 325931972BR 6949NW21-062BLaimer MSancere LFrohlich ADengler SBozek KPersa ODLandsberg JBragelmann JKreuter ALang RLorenz CHelbig DfalseHisto-Miner: Deep learning based tissue features extraction pipeline from H&E whole slide images of cutaneous squamous cell carcinoma.Recent advances in digital pathology have enabled comprehensive analyses of Whole-Slide Images (WSIs) from tissue samples, leveraging high-resolution microscopy and computational capabilities. Despite this progress, available tools for automatic cell type identification perform poorly on skin tissue, e.g. in the classification of non-melanoma tumor cells. This is due to a paucity of labeled training data sets and high morphological similarities between tumor and non-tumor epithelial cells in the skin. Here, we propose Histo-Miner, a deep learning-based pipeline designed for the analysis of skin WSIs. To this end we generated two new datasets using WSIs of cutaneous Squamous Cell Carcinoma (cSCC) samples, a frequent non-melanoma skin cancer, by annotating 47,392 cell nuclei across 5 cell ty2026-01-01T00:00:00Z2026 Jan2026-06-11T06:04:56.919Z2026-06-11T03:12:19.972ZS-EPMC128544734156412010.1371/journal.pcbi.1013907