Frederic Madesta

1.0k citations
19 papers · 563 · h-index 10

Impact in

Papers in

Frederic Madesta

18 papers receiving 557 citations

Peers

Frederic Madesta
Comparison fields: 5 of 62
  • Radiology, Nuclear Medicine and Imaging 368
  • Radiation 131
  • Health Informatics 19
  • Artificial Intelligence 169
  • Oncology 130
Replace Changjuan Tao with:
Changjuan Tao China
Siri Willems Belgium
Elisa Scalco Italy
Sebastian R. van der Voort Netherlands
Reza Farjam United States
Kanabu Nawa Japan
Mireia Crispin‐Ortuzar United Kingdom
Wookjin Choi United States
Qingtao Qiu China
D Khullar United States
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Citations per field
00.5×2.6×
Changjuan Tao · 1×
Citations per year

Countries citing papers authored by Frederic Madesta

Since Specialization
Citations

This map shows the geographic impact of Frederic Madesta'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 Frederic Madesta with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Frederic Madesta more than expected).

Fields of papers citing papers by Frederic Madesta

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Frederic Madesta. 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 Frederic Madesta. The network helps show where Frederic Madesta may publish in the future.

Co-authors

The 25 scholars most cited alongside Frederic Madesta, 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 Frederic Madesta Line = papers co-authored together Frederic Madesta links everyone, so they are left out of the graph.

All Works

19 of 19 papers shown
#Work
1 2018176
2 2019125
3 202170
4 202033
5 202429
6 201927
7 202027
8 202021
9 202017
10 202214
11 20239
12 20195
13 20183
14 20232
15 20242
16 20241
17
Abstract: Widening the Focus - Biomedical Image Segmentation Challenges and the Underestimated Role of Patch Sampling and Inference Strategies.
20211
18 20181
19 20190

About Frederic Madesta

Frederic Madesta is a scholar working on Radiology, Nuclear Medicine and Imaging, Radiation, Biomedical Engineering, Pulmonary and Respiratory Medicine and Artificial Intelligence, having authored 19 papers that have together received 563 indexed citations. Recurring topics across this work include Medical Imaging Techniques and Applications (14 papers), Advanced Radiotherapy Techniques (13 papers), Advanced X-ray and CT Imaging (7 papers), Radiomics and Machine Learning in Medical Imaging (6 papers), AI in cancer detection (3 papers), Radiation Therapy and Dosimetry (2 papers), Brain Metastases and Treatment (1 paper) and Cutaneous Melanoma Detection and Management (1 paper). The work is most often cited by research in Radiology, Nuclear Medicine and Imaging (368 citations), Radiation (131 citations), Health Informatics (19 citations), Artificial Intelligence (169 citations) and Oncology (130 citations). Frederic Madesta has collaborated with scholars based in Germany, United States and Canada. Frequent co-authors include René Werner, Tobias Gauer, Thilo Sentker, Helge Kniep, Rüdiger Schmitz, Uta Hanning, Gerhard Schön, Susanne Gellißen, Michael Schönfeld and Jens Fiehler. Their work appears in journals such as Medical Physics, Radiotherapy and Oncology, Cancers, Radiology and IEEE Transactions on Biomedical Engineering.

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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