no code implementations • 7 Jul 2023 • Xiaoyi Ji, Richard Salmon, Nita Mulliqi, Umair Khan, Yinxi Wang, Anders Blilie, Henrik Olsson, Bodil Ginnerup Pedersen, Karina Dalsgaard Sørensen, Benedicte Parm Ulhøi, Svein R Kjosavik, Emilius AM Janssen, Mattias Rantalainen, Lars Egevad, Pekka Ruusuvuori, Martin Eklund, Kimmo Kartasalo
The potential of artificial intelligence (AI) in digital pathology is limited by technical inconsistencies in the production of whole slide images (WSIs), leading to degraded AI performance and posing a challenge for widespread clinical application as fine-tuning algorithms for each new site is impractical.
no code implementations • 27 Jun 2021 • Bojing Liu, Yinxi Wang, Philippe Weitz, Johan Lindberg, Johan Hartman, Lars Egevad, Henrik Grönberg, Martin Eklund, Mattias Rantalainen
As a proof-of-principle, we developed and validated a deep convolutional neural network model to distinguish between morphological patterns in benign prostate biopsy whole slide images from men with and without established cancer.
1 code implementation • 19 Apr 2021 • Philippe Weitz, Yinxi Wang, Kimmo Kartasalo, Lars Egevad, Johan Lindberg, Henrik Grönberg, Martin Eklund, Mattias Rantalainen
Molecular phenotyping by gene expression profiling is common in contemporary cancer research and in molecular diagnostics.
no code implementations • 18 Sep 2020 • Yinxi Wang, Kimmo Kartasalo, Masi Valkonen, Christer Larsson, Pekka Ruusuvuori, Johan Hartman, Mattias Rantalainen
The relationship between morphology and molecular phenotype has a potential to be exploited for prediction of the molecular phenotype from the morphology visible in histopathology images.