Tootfinder

Generalisation of automatic tumour segmentation in histopathological whole-slide images across multiple cancer types
Ole-Johan Skrede, Manohar Pradhan, Maria Xepapadakis Isaksen, Tarjei Sveinsgjerd Hveem, Ljiljana Vlatkovic, Arild Nesbakken, Kristina Lindemann, Gunnar B Kristensen, Jenneke Kasius, Alain G Zeimet, Odd Terje Brustugun, Lill-Tove Rasmussen Busund, Elin H Richardsen, Erik Skaaheim Haug, Bj{\o}rn Brennhovd, Emma Rewcastle, Melinda Lillesand, Vebj{\o}rn Kvikstad, Emiel Janss…

Generalisation of automatic tumour segmentation in histopathological whole-slide images across multiple cancer types
Deep learning is expected to aid pathologists by automating tasks such as tumour segmentation. We aimed to develop one universal tumour segmentation model for histopathological images and examine its performance in different cancer types. The model was developed using over 20 000 whole-slide images from over 4 000 patients with colorectal, endometrial, lung, or prostate carcinoma. Performance was validated in pre-planned analyses on external cohorts with over 3 000 patients across six cancer ty…

Machine-interpretable Engineering Design Standards for Valve Specification
Anders Gjerver, Rune Frostad, Vedrana Barisic, Melinda Hodkiewicz, Caitlin Woods, Mihaly Fekete, Arild Braathen Torjusen, Johan Wilhelm Kluwer
https://arxiv.org/abs/2510.01736

Machine-interpretable Engineering Design Standards for Valve Specification
Engineering design processes use technical specifications and must comply with standards. Product specifications, product type data sheets, and design standards are still mainly document-centric despite the ambition to digitalize industrial work. In this paper, we demonstrate how to transform information held in engineering design standards into modular, reusable, machine-interpretable ontologies and use the ontologies in quality assurance of the plant design and equipment selection process. We…