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20172024
most citedMitosis domain generalization in histopathology images -- The MIDOG challenge

162 citations · 455 across the 51 of their papers we have counts for

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Showing 2022Show all

6 papers · 1 filter

cs.CV2022★ 4 cited

Joint segmentation and discontinuity-preserving deformable registration: Application to cardiac cine-MR images

Xiang Chen, Yan Xia, Nishant Ravikumar +1

Medical image registration is a challenging task involving the estimation of spatial transformations to establish anatomical correspondence between pairs or groups of images. Recen…

eess.IV2022★ 2 cited

A Generative Shape Compositional Framework to Synthesise Populations of Virtual Chimaeras

Haoran Dou, Seppo Virtanen, Nishant Ravikumar +1

Generating virtual populations of anatomy that capture sufficient variability while remaining plausible is essential for conducting in-silico trials of medical devices. However, no…

cs.CV2022

Agent with Tangent-based Formulation and Anatomical Perception for Standard Plane Localization in 3D Ultrasound

Yuxin Zou, Haoran Dou, Yuhao Huang +9

Standard plane (SP) localization is essential in routine clinical ultrasound (US) diagnosis. Compared to 2D US, 3D US can acquire multiple view planes in one scan and provide compl…

eess.IV2022

Localizing the Recurrent Laryngeal Nerve via Ultrasound with a Bayesian Shape Framework

Haoran Dou, Luyi Han, Yushuang He +6

Tumor infiltration of the recurrent laryngeal nerve (RLN) is a contraindication for robotic thyroidectomy and can be difficult to detect via standard laryngoscopy. Ultrasound (US)…

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…

cs.CV2022★ 2 cited

Epistemic Uncertainty-Weighted Loss for Visual Bias Mitigation

Rebecca S Stone, Nishant Ravikumar, Andrew J Bulpitt +1

Deep neural networks are highly susceptible to learning biases in visual data. While various methods have been proposed to mitigate such bias, the majority require explicit knowled…