activity
20162026
most citedAutomated Design of Deep Learning Methods for Biomedical Image Segmentation

9.8k citations · 18.5k across the 47 of their papers we have counts for

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Showing 2023 · cs.CVShow all

7 papers · 2 filters

cs.CV2023★ 1 cited

RecycleNet: Latent Feature Recycling Leads to Iterative Decision Refinement

Gregor Koehler, Tassilo Wald, Constantin Ulrich +6

Despite the remarkable success of deep learning systems over the last decade, a key difference still remains between neural network and human decision-making: As humans, we cannot…

cs.CV2023★ 35 cited

The KiTS21 Challenge: Automatic segmentation of kidneys, renal tumors, and renal cysts in corticomedullary-phase CT

Nicholas Heller, Fabian Isensee, Dasha Trofimova +42

This paper presents the challenge report for the 2021 Kidney and Kidney Tumor Segmentation Challenge (KiTS21) held in conjunction with the 2021 international conference on Medical…

cs.CV2023★ 1 cited

Transformer Utilization in Medical Image Segmentation Networks

Saikat Roy, Gregor Koehler, Michael Baumgartner +4

Owing to success in the data-rich domain of natural images, Transformers have recently become popular in medical image segmentation. However, the pairing of Transformers with convo…

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.CV2023★ 204 cited

Understanding metric-related pitfalls in image analysis validation

Annika Reinke, Minu D. Tizabi, Michael Baumgartner +75

Validation metrics are key for the reliable tracking of scientific progress and for bridging the current chasm between artificial intelligence (AI) research and its translation int…

cs.CV2023★ 5 cited

CRADL: Contrastive Representations for Unsupervised Anomaly Detection and Localization

Carsten T. Lüth, David Zimmerer, Gregor Koehler +4

Unsupervised anomaly detection in medical imaging aims to detect and localize arbitrary anomalies without requiring annotated anomalous data during training. Often, this is achieve…

cs.CV2023★ 2 cited

ParticleSeg3D: A Scalable Out-of-the-Box Deep Learning Segmentation Solution for Individual Particle Characterization from Micro CT Images in Mineral Processing and Recycling

Karol Gotkowski, Shuvam Gupta, Jose R. A. Godinho +3

Minerals, metals, and plastics are indispensable for a functioning modern society. Yet, their supply is limited causing a need for optimizing ore extraction and recuperation from r…