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20162024
most citedOffline Writer Identification Using Convolutional Neural Network Activation Features

66 citations · 366 across the 74 of their papers we have counts for

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Showing 2021Show all

18 papers · 1 filter

eess.IV2021

Automatic Plane Adjustment of Orthopedic Intra-operative Flat Panel Detector CT-Volumes

Celia Martin Vicario, Florian Kordon, Felix Denzinger +7

Purpose 3D acquisitions are often acquired to assess the result in orthopedic trauma surgery. With a mobile C-Arm system, these acquisitions can be performed intra-operatively. Tha…

cs.HC2021

Inter-Species Cell Detection: Datasets on pulmonary hemosiderophages in equine, human and feline specimens

Christian Marzahl, Jenny Hill, Jason Stayt +9

Pulmonary hemorrhage (P-Hem) occurs among multiple species and can have various causes. Cytology of bronchoalveolarlavage fluid (BALF) using a 5-tier scoring system of alveolar mac…

eess.IV20212 cited

Automatic and explainable grading of meningiomas from histopathology images

Jonathan Ganz, Tobias Kirsch, Lucas Hoffmann +7

Meningioma is one of the most prevalent brain tumors in adults. To determine its malignancy, it is graded by a pathologist into three grades according to WHO standards. This grade…

cs.CV2021

Robust Classification from Noisy Labels: Integrating Additional Knowledge for Chest Radiography Abnormality Assessment

Sebastian Gündel, Arnaud A. A. Setio, Florin C. Ghesu +4

Chest radiography is the most common radiographic examination performed in daily clinical practice for the detection of various heart and lung abnormalities. The large amount of da…

cs.CV202119 cited

Quantifying the Scanner-Induced Domain Gap in Mitosis Detection

Marc Aubreville, Christof Bertram, Mitko Veta +6

Automated detection of mitotic figures in histopathology images has seen vast improvements, thanks to modern deep learning-based pipelines. Application of these methods, however, i…

cs.CV2021

Adapt Everywhere: Unsupervised Adaptation of Point-Clouds and Entropy Minimisation for Multi-modal Cardiac Image Segmentation

Sulaiman Vesal, Mingxuan Gu, Ronak Kosti +2

Deep learning models are sensitive to domain shift phenomena. A model trained on images from one domain cannot generalise well when tested on images from a different domain, despit…