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Found 6 papers, 2 papers with code
Re-identification from histopathology images
We predicted the source patient of a slide with F1 scores of 50. 16 % and 52. 30 % on the LSCC and LUAD datasets, respectively, and with 62. 31 % on our meningioma dataset.
Domain generalization across tumor types, laboratories, and species -- insights from the 2022 edition of the Mitosis Domain Generalization Challenge
The challenge provided annotated histologic tumor images from six different domains and evaluated the algorithmic approaches for mitotic figure detection provided by nine challenge participants on ten independent domains.
SYNTA: A novel approach for deep learning-based image analysis in muscle histopathology using photo-realistic synthetic data
Here we introduce SYNTA (synthetic data) as a novel approach for the generation of synthetic, photo-realistic, and highly complex biomedical images as training data for DL systems.
Automatic and explainable grading of meningiomas from histopathology images
In a first approach, image patches are sampled from this region and regression is based on morphological features encoded by a ResNet-based network.
Learning New Tricks from Old Dogs -- Inter-Species, Inter-Tissue Domain Adaptation for Mitotic Figure Assessment
We were able to show that domain adversarial training considerably improves accuracy when applying mitotic figure classification learned from the canine on the human data sets (up to +12. 8% in accuracy) and is thus a helpful method to transfer knowledge from existing data sets to new tissue types and species.
Deep Learning-Based Quantification of Pulmonary Hemosiderophages in Cytology Slides
Additionally, we evaluated object detection methods on a novel data set of 17 completely annotated cytology whole slide images (WSI) containing 78, 047 hemosiderophages.
