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Found 3 papers, 2 papers with code
Strategies to Improve Real-World Applicability of Laparoscopic Anatomy Segmentation Models
Accurate identification and localization of anatomical structures of varying size and appearance in laparoscopic imaging are necessary to leverage the potential of computer vision techniques for surgical decision support.
One model to use them all: Training a segmentation model with complementary datasets
In this work, we propose a method to combine multiple partially annotated datasets, which provide complementary annotations, into one model, enabling better scene segmentation and the use of multiple readily available datasets.
Regression-based Deep-Learning predicts molecular biomarkers from pathology slides
We tested our method for multiple clinically and biologically relevant biomarkers: homologous repair deficiency (HRD) score, a clinically used pan-cancer biomarker, as well as markers of key biological processes in the tumor microenvironment.
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