activity
20202026
most citedMedical Deep Learning -- A systematic Meta-Review

259 citations · 413 across the 24 of their papers we have counts for

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10 papers · 1 filter

cs.CV2026

Globally Optimal Pose from Orthographic Silhouettes

Agniva Sengupta, Dilara Kuş, Jianning Li +1

We solve the problem of determining the pose of known shapes in from their unoccluded silhouettes. The pose is determined up to global optimality using a simple yet…

cs.CV2024

Deep Medial Voxels: Learned Medial Axis Approximations for Anatomical Shape Modeling

Antonio Pepe, Richard Schussnig, Jianning Li +3

Shape reconstruction from imaging volumes is a recurring need in medical image analysis. Common workflows start with a segmentation step, followed by careful post-processing and,fi…

cs.CV2024★ 10 cited

Cyto R-CNN and CytoNuke Dataset: Towards reliable whole-cell segmentation in bright-field histological images

Johannes Raufeisen, Kunpeng Xie, Fabian Hörst +9

Background: Cell segmentation in bright-field histological slides is a crucial topic in medical image analysis. Having access to accurate segmentation allows researchers to examine…

cs.CV2023★ 35 cited

MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision

Jianning Li, Zongwei Zhou, Jiancheng Yang +154

Prior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from co…

cs.CV2023★ 1 cited

Why is the winner the best?

Matthias Eisenmann, Annika Reinke, Vivienn Weru +122

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to in…

cs.CV2022★ 17 cited

Biomedical image analysis competitions: The state of current participation practice

Matthias Eisenmann, Annika Reinke, Vivienn Weru +352

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known abou…

cs.CV2022★ 1 cited

Training β-VAE by Aggregating a Learned Gaussian Posterior with a Decoupled Decoder

Jianning Li, Jana Fragemann, Seyed-Ahmad Ahmadi +2

The reconstruction loss and the Kullback-Leibler divergence (KLD) loss in a variational autoencoder (VAE) often play antagonistic roles, and tuning the weight of the KLD loss in $β…

cs.CV2022★ 45 cited

FakeNews: GAN-based generation of realistic 3D volumetric data -- A systematic review and taxonomy

André Ferreira, Jianning Li, Kelsey L. Pomykala +3

With the massive proliferation of data-driven algorithms, such as deep learning-based approaches, the availability of high-quality data is of great interest. Volumetric data is ver…

cs.CV2022★ 1 cited

Back to the Roots: Reconstructing Large and Complex Cranial Defects using an Image-based Statistical Shape Model

Jianning Li, David G. Ellis, Antonio Pepe +4

Designing implants for large and complex cranial defects is a challenging task, even for professional designers. Current efforts on automating the design process focused mainly on…

cs.CV2020★ 8 cited

A Baseline Approach for AutoImplant: the MICCAI 2020 Cranial Implant Design Challenge

Jianning Li, Antonio Pepe, Christina Gsaxner +2

In this study, we present a baseline approach for AutoImplant (https://autoimplant.grand-challenge.org/) - the cranial implant design challenge, which, as suggested by the organize…