Katja Siegmann-Luz

523 citations
22 papers · 307 indexed · 1 hit paper · h-index 10

Katja Siegmann-Luz

21 papers receiving 301 citations

Hit Papers

Nationwide real-world implementation of AI for cancer det...482025202610203040

Peers

Katja Siegmann-Luz
Comparison fields: 5 of 39
  • Health Informatics 16
  • Radiology, Nuclear Medicine and Imaging 164
  • Obstetrics and Gynecology 50
  • Pathology and Forensic Medicine 95
  • Cancer Research 78
Replace Mohiedean Ghofrani with:
Mohiedean Ghofrani United States
Latifa Fellah Belgium
Gordon Wright Australia
Xavier Caparrós Spain
Annarita Gencarelli Italy
Fuki Shitano Japan
Sang Yu Nam South Korea
H.D. Zakhour United Kingdom
Xiaping Chen China
Glen Lo Australia
Katja Siegmann-Luz relative to Mohiedean Ghofrani United States Mohiedean Ghofrani's profile →
Citations per field
00.5×8.5×
Mohiedean Ghofrani · 1×
Citations per year

Countries citing papers authored by Katja Siegmann-Luz

Since Specialization
Citations

This map shows the geographic impact of Katja Siegmann-Luz's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Katja Siegmann-Luz with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Katja Siegmann-Luz more than expected).

Fields of papers citing papers by Katja Siegmann-Luz

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Katja Siegmann-Luz. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Katja Siegmann-Luz. The network helps show where Katja Siegmann-Luz may publish in the future.

Co-authorship network

The 25 scholars most cited alongside Katja Siegmann-Luz, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Katja Siegmann-Luz Line = papers co-authored together Katja Siegmann-Luz links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1
Nationwide real-world implementation of AI for cancer detection in population-based mammography screeningbreakdown →
202548
2 20241
3 20241
4 20220
5 20212
6 20211
7 20198
8 201813
9 20186
10 20178
11 201561
12 20154
13 201417
14 20149
15 201449
16 20141
17 20142
18 20137
19 201333
20 20139

About Katja Siegmann-Luz

Katja Siegmann-Luz is a scholar working on Radiology, Nuclear Medicine and Imaging, Cancer Research and Pathology and Forensic Medicine, having authored 22 papers that have together received 307 indexed citations. Recurring topics across this work include MRI in cancer diagnosis (9 papers), Radiomics and Machine Learning in Medical Imaging (8 papers), Breast Cancer Treatment Studies (7 papers), Breast Lesions and Carcinomas (6 papers), Global Cancer Incidence and Screening (5 papers), AI in cancer detection (4 papers), Advanced MRI Techniques and Applications (4 papers) and Digital Radiography and Breast Imaging (4 papers). The work is most often cited by research in Health Informatics (16 citations), Radiology, Nuclear Medicine and Imaging (164 citations) and Obstetrics and Gynecology (50 citations). Katja Siegmann-Luz has collaborated with scholars based in Germany, Austria and Switzerland. Frequent co-authors include Heike Preibsch, Annette Staebler, Beate Wietek, Markus Hahn, Claus D. Claussen, Sara Y. Brucker, Katharina Rall, Konstantin Nikolaou, Bernhard Krämer and Benjamin Wiesinger. Their work appears in journals such as Nature Medicine, European Radiology and European Journal of Radiology.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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