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20172026
most citedMitosis domain generalization in histopathology images -- The MIDOG challenge

162 citations · 625 across the 28 of their papers we have counts for

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8 papers · 1 filter

eess.IV2023★ 2 cited

Multi-Scanner Canine Cutaneous Squamous Cell Carcinoma Histopathology Dataset

Frauke Wilm, Marco Fragoso, Christof A. Bertram +7

In histopathology, scanner-induced domain shifts are known to impede the performance of trained neural networks when tested on unseen data. Multi-domain pre-training or dedicated d…

eess.IV2022

Deep Learning-Based Automatic Assessment of AgNOR-scores in Histopathology Images

Jonathan Ganz, Karoline Lipnik, Jonas Ammeling +10

Nucleolar organizer regions (NORs) are parts of the DNA that are involved in RNA transcription. Due to the silver affinity of associated proteins, argyrophilic NORs (AgNORs) can be…

eess.IV2022

Mind the Gap: Scanner-induced domain shifts pose challenges for representation learning in histopathology

Frauke Wilm, Marco Fragoso, Christof A. Bertram +7

Computer-aided systems in histopathology are often challenged by various sources of domain shift that impact the performance of these algorithms considerably. We investigated the p…

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.IV2022★ 31 cited

Pan-tumor CAnine cuTaneous Cancer Histology (CATCH) dataset

Frauke Wilm, Marco Fragoso, Christian Marzahl +8

Due to morphological similarities, the differentiation of histologic sections of cutaneous tumors into individual subtypes can be challenging. Recently, deep learning-based approac…

eess.IV2021

First steps on Gamification of Lung Fluid Cells Annotations in the Flower Domain

Sonja Kunzmann, Christian Marzahl, Felix Denzinger +5

Annotating data, especially in the medical domain, requires expert knowledge and a lot of effort. This limits the amount and/or usefulness of available medical data sets for experi…

eess.IV2021★ 1 cited

Learning to be EXACT, Cell Detection for Asthma on Partially Annotated Whole Slide Images

Christian Marzahl, Christof A. Bertram, Frauke Wilm +6

Asthma is a chronic inflammatory disorder of the lower respiratory tract and naturally occurs in humans and animals including horses. The annotation of an asthma microscopy whole s…

eess.IV2019★ 63 cited

Deep Learning-Based Quantification of Pulmonary Hemosiderophages in Cytology Slides

Christian Marzahl, Marc Aubreville, Christof A. Bertram +13

Purpose: Exercise-induced pulmonary hemorrhage (EIPH) is a common syndrome in sport horses with negative impact on performance. Cytology of bronchoalveolar lavage fluid by use of a…