Annika Reinke
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Explore the profile of Annika Reinke including associated specialties, affiliations and a list of published articles.
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21
Citations
437
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Recent Articles
1.
Godau P, Kalinowski P, Christodoulou E, Reinke A, Tizabi M, Ferrer L, et al.
Med Image Anal
. 2025 Feb;
102:103504.
PMID: 40020420
Domain gaps are significant obstacles to the clinical implementation of machine learning (ML) solutions for medical image analysis. Although current research emphasizes new training methods and network architectures, the specific...
2.
Adler T, Nolke J, Reinke A, Tizabi M, Gruber S, Trofimova D, et al.
Med Image Anal
. 2025 Feb;
101:103474.
PMID: 39892221
Current deep learning-based solutions for image analysis tasks are commonly incapable of handling problems to which multiple different plausible solutions exist. In response, posterior-based methods such as conditional Diffusion Models...
3.
Cimini B, Bankhead P, DAntuono R, Fazeli E, Fernandez-Rodriguez J, Fuster-Barcelo C, et al.
J Cell Sci
. 2024 Oct;
137(20).
PMID: 39475207
Bioimage analysis (BIA), a crucial discipline in biological research, overcomes the limitations of subjective analysis in microscopy through the creation and application of quantitative and reproducible methods. The establishment of...
4.
Floca R, Bohn J, Haux C, Wiestler B, Zollner F, Reinke A, et al.
Insights Imaging
. 2024 Jun;
15(1):124.
PMID: 38825600
Objectives: Achieving a consensus on a definition for different aspects of radiomics workflows to support their translation into clinical usage. Furthermore, to assess the perspective of experts on important challenges...
5.
Maier-Hein L, Reinke A, Godau P, Tizabi M, Buettner F, Christodoulou E, et al.
Nat Methods
. 2024 Feb;
21(2):195-212.
PMID: 38347141
Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. In biomedical image analysis, chosen performance metrics often do not reflect the domain interest,...
6.
Reinke A, Tizabi M, Baumgartner M, Eisenmann M, Heckmann-Notzel D, Kavur A, et al.
Nat Methods
. 2024 Feb;
21(2):182-194.
PMID: 38347140
Validation metrics are key for tracking scientific progress and bridging the current chasm between artificial intelligence research and its translation into practice. However, increasing evidence shows that, particularly in image...
7.
Brandenburg J, Jenke A, Stern A, Daum M, Schulze A, Younis R, et al.
Surg Endosc
. 2023 Oct;
37(11):8577-8593.
PMID: 37833509
Background: With Surgomics, we aim for personalized prediction of the patient's surgical outcome using machine-learning (ML) on multimodal intraoperative data to extract surgomic features as surgical process characteristics. As high-quality...
8.
Ross T, Bruno P, Reinke A, Wiesenfarth M, Koeppel L, Full P, et al.
Med Image Anal
. 2023 Mar;
86:102765.
PMID: 36965252
Challenges have become the state-of-the-art approach to benchmark image analysis algorithms in a comparative manner. While the validation on identical data sets was a great step forward, results analysis is...
9.
Reinke A, Tizabi M, Baumgartner M, Eisenmann M, Heckmann-Notzel D, Kavur A, et al.
ArXiv
. 2023 Mar;
PMID: 36945687
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 into practice. However, increasing evidence...
10.
Wagner M, Muller-Stich B, Kisilenko A, Tran D, Heger P, Mundermann L, et al.
Med Image Anal
. 2023 Mar;
86:102770.
PMID: 36889206
Purpose: Surgical workflow and skill analysis are key technologies for the next generation of cognitive surgical assistance systems. These systems could increase the safety of the operation through context-sensitive warnings...