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20162024
most citedA Survey on Deep Learning in Medical Image Analysis

15.4k citations · 17.4k across the 18 of their papers we have counts for

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6 papers · 1 filter

eess.IV2023

LYSTO: The Lymphocyte Assessment Hackathon and Benchmark Dataset

Yiping Jiao, Jeroen van der Laak, Shadi Albarqouni +18

We introduce LYSTO, the Lymphocyte Assessment Hackathon, which was held in conjunction with the MICCAI 2019 Conference in Shenzen (China). The competition required participants to…

eess.IV2022★ 162 cited

Mitosis domain generalization in histopathology images -- The MIDOG challenge

Marc Aubreville, Nikolas Stathonikos, Christof A. Bertram +32

The density of mitotic figures within tumor tissue is known to be highly correlated with tumor proliferation and thus is an important marker in tumor grading. Recognition of mitoti…

eess.IV2020★ 1 cited

Automated Scoring of Nuclear Pleomorphism Spectrum with Pathologist-level Performance in Breast Cancer

Caner Mercan, Maschenka Balkenhol, Roberto Salgado +13

Nuclear pleomorphism, defined herein as the extent of abnormalities in the overall appearance of tumor nuclei, is one of the components of the three-tiered breast cancer grading. G…

eess.IV2020

HookNet: multi-resolution convolutional neural networks for semantic segmentation in histopathology whole-slide images

Mart van Rijthoven, Maschenka Balkenhol, Karina Siliņa +2

We propose HookNet, a semantic segmentation model for histopathology whole-slide images, which combines context and details via multiple branches of encoder-decoder convolutional n…

eess.IV2020★ 19 cited

Extending Unsupervised Neural Image Compression With Supervised Multitask Learning

David Tellez, Diederik Hoppener, Cornelis Verhoef +5

We focus on the problem of training convolutional neural networks on gigapixel histopathology images to predict image-level targets. For this purpose, we extend Neural Image Compre…

eess.IV2020

Virtual staining for mitosis detection in Breast Histopathology

Caner Mercan, Germonda Reijnen-Mooij, David Tellez Martin +4

We propose a virtual staining methodology based on Generative Adversarial Networks to map histopathology images of breast cancer tissue from H&E stain to PHH3 and vice versa. We us…