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20202025
most citedUltra Low-Parameter Denoising: Trainable Bilateral Filter Layers in Computed Tomography

30 citations · 102 across the 23 of their papers we have counts for

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6 papers · 1 filter

cs.CV2025

CyclePose -- Leveraging Cycle-Consistency for Annotation-Free Nuclei Segmentation in Fluorescence Microscopy

Jonas Utz, Stefan Vocht, Anne Tjorven Buessen +6

In recent years, numerous neural network architectures specifically designed for the instance segmentation of nuclei in microscopic images have been released. These models embed nu…

cs.CV2024

Differentiable Score-Based Likelihoods: Learning CT Motion Compensation From Clean Images

Mareike Thies, Noah Maul, Siyuan Mei +8

Motion artifacts can compromise the diagnostic value of computed tomography (CT) images. Motion correction approaches require a per-scan estimation of patient-specific motion patte…

cs.CV2024

BigReg: An Efficient Registration Pipeline for High-Resolution X-Ray and Light-Sheet Fluorescence Microscopy

Siyuan Mei, Fuxin Fan, Mareike Thies +9

Recently, X-ray microscopy (XRM) and light-sheet fluorescence microscopy (LSFM) have emerged as pivotal tools in preclinical research, particularly for studying bone remodeling dis…

cs.CV2023

Exploring Epipolar Consistency Conditions for Rigid Motion Compensation in In-vivo X-ray Microscopy

Mareike Thies, Fabian Wagner, Mingxuan Gu +10

Intravital X-ray microscopy (XRM) in preclinical mouse models is of vital importance for the identification of microscopic structural pathological changes in the bone which are cha…

cs.CV2022

ConFUDA: Contrastive Fewshot Unsupervised Domain Adaptation for Medical Image Segmentation

Mingxuan Gu, Sulaiman Vesal, Mareike Thies +5

Unsupervised domain adaptation (UDA) aims to transfer knowledge learned from a labeled source domain to an unlabeled target domain. Contrastive learning (CL) in the context of UDA…

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…