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Found 4 papers, 2 papers with code
Focused Active Learning for Histopathological Image Classification
The lack of precise uncertainty estimations leads to the acquisition of images with a low informative value.
MONAI: An open-source framework for deep learning in healthcare
For AI models to be used clinically, they need to be made safe, reproducible and robust, and the underlying software framework must be aware of the particularities (e. g. geometry, physiology, physics) of medical data being processed.
A Histopathology Study Comparing Contrastive Semi-Supervised and Fully Supervised Learning
In this paper we explore self-supervised learning to reduce labeling burdens in computational pathology.
NuCLS: A scalable crowdsourcing, deep learning approach and dataset for nucleus classification, localization and segmentation
High-resolution mapping of cells and tissue structures provides a foundation for developing interpretable machine-learning models for computational pathology.
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