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Found 5 papers, 3 papers with code
Self-Contrastive Weakly Supervised Learning Framework for Prognostic Prediction Using Whole Slide Images
We present a pioneering investigation into the application of deep learning techniques to analyze histopathological images for addressing the substantial challenge of automated prognostic prediction.
Minimally Interactive Segmentation of Soft-Tissue Tumors on CT and MRI using Deep Learning
Next, the method was externally validated on a dataset including five unseen STT phenotypes in extremities, achieving 0. 81$\pm$0. 08 for CT, 0. 84$\pm$0. 09 for T1-weighted MRI, and 0. 88\pm0. 08 for previously unseen T2-weighted fat-saturated (FS) MRI.
Reproducible radiomics through automated machine learning validated on twelve clinical applications
In this study we propose a framework for automatically optimizing the construction of radiomics workflows per application.
Differential diagnosis and molecular stratification of gastrointestinal stromal tumors on CT images using a radiomics approach
The aim of this study was to evaluate radiomics for distinguishing GISTs from other intra-abdominal tumors, and in GISTs, predict the c-KIT, PDGFRA, BRAF mutational status and mitotic index (MI).
Automated Detection of Cribriform Growth Patterns in Prostate Histology Images
In conclusion, the proposed deep learning method has high sensitivity for detecting cribriform growth patterns at the expense of a limited number of false positives.
