0000000001220461

AUTHOR

Liisa Petäinen

showing 1 related works from this author

Domain-specific transfer learning in the automated scoring of tumor-stroma ratio from histopathological images of colorectal cancer

2023

Tumor-stroma ratio (TSR) is a prognostic factor for many types of solid tumors. In this study, we propose a method for automated estimation of TSR from histopathological images of colorectal cancer. The method is based on convolutional neural networks which were trained to classify colorectal cancer tissue in hematoxylin-eosin stained samples into three classes: stroma, tumor and other. The models were trained using a data set that consists of 1343 whole slide images. Three different training setups were applied with a transfer learning approach using domain-specific data i.e. an external colorectal cancer histopathological data set. The three most accurate models were chosen as a classifie…

FOS: Computer and information sciencessmooth musclesvisionComputer Science - Machine LearningMultidisciplinaryComputer Vision and Pattern Recognition (cs.CV)Computer Science - Computer Vision and Pattern Recognitioncolorectal cancerforecastingennusteetneuroverkotsuolistosyövätneural networksQuantitative Biology - Quantitative MethodsMachine Learning (cs.LG)machine learningkoneoppiminenFOS: Biological sciencessyöpätauditcancers and neoplasmsmalignant tumorsQuantitative Methods (q-bio.QM)
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