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Adapting Self-Supervised Learning for Computational Pathology
Self-supervised learning (SSL) has emerged as a key technique for training networks that can generalize well to diverse tasks without task-specific supervision.
Benchmarking a Benchmark: How Reliable is MS-COCO?
Benchmark datasets are used to profile and compare algorithms across a variety of tasks, ranging from image classification to segmentation, and also play a large role in image pretraining algorithms.
An Empirical Study of Uncertainty in Polygon Annotation and the Impact of Quality Assurance
Polygons are a common annotation format used for quickly annotating objects in instance segmentation tasks.
Virchow: A Million-Slide Digital Pathology Foundation Model
The use of artificial intelligence to enable precision medicine and decision support systems through the analysis of pathology images has the potential to revolutionize the diagnosis and treatment of cancer.
Surface Denoising based on Normal Filtering in a Robust Statistics Framework
In this paper, we introduce such a framework to establish relations between a number of widely-used nonlinear filters for face normals in mesh denoising and vertex normals in point set denoising.
