Benchmarking a Benchmark: How Reliable is MS-COCO?

no code implementations • 5 Nov 2023 • Eric Zimmermann, Justin Szeto, Jerome Pasquero, Frederic Ratle

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.

Benchmarking Image Classification

An Empirical Study of Uncertainty in Polygon Annotation and the Impact of Quality Assurance

no code implementations • 5 Nov 2023 • Eric Zimmermann, Justin Szeto, Frederic Ratle

Polygons are a common annotation format used for quickly annotating objects in instance segmentation tasks.

Instance Segmentation Semantic Segmentation

Virchow: A Million-Slide Digital Pathology Foundation Model

no code implementations • 14 Sep 2023 • Eugene Vorontsov, Alican Bozkurt, Adam Casson, George Shaikovski, Michal Zelechowski, SiQi Liu, Kristen Severson, Eric Zimmermann, James Hall, Neil Tenenholtz, Nicolo Fusi, Philippe Mathieu, Alexander van Eck, Donghun Lee, Julian Viret, Eric Robert, Yi Kan Wang, Jeremy D. Kunz, Matthew C. H. Lee, Jan Bernhard, Ran A. Godrich, Gerard Oakley, Ewan Millar, Matthew Hanna, Juan Retamero, William A. Moye, Razik Yousfi, Christopher Kanan, David Klimstra, Brandon Rothrock, Thomas J. Fuchs

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.

Self-Supervised Learning whole slide images

Surface Denoising based on Normal Filtering in a Robust Statistics Framework

no code implementations • 2 Jul 2020 • Sunil Kumar Yadav, Martin Skrodzki, Eric Zimmermann, Konrad Polthier

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.

Denoising