Tokiya Abe

868 citations
50 papers · 681 · h-index 16

Impact in

  • Biophysics top 5%
    • Cell Image Analysis Techniques
  • Hepatology top 10%
    • Hepatocellular Carcinoma Treatment and Prognosis

Papers in

Tokiya Abe

49 papers receiving 662 citations

Peers

Tokiya Abe
Comparison fields: 5 of 80
  • Biophysics 96
  • Hepatology 88
  • Computer Vision and Pattern Recognition 149
  • Oncology 146
  • Artificial Intelligence 168
Replace Emmanouil Athanasiadis with:
Emmanouil Athanasiadis Greece
Marios A. Gavrielides United States
Marco Wiltgen Austria
Dmitrii Bychkov Finland
Hesham Eldaly United Kingdom
Zhenwei Shi China
Benoît Schmauch France
Pierre Courtiol France
W. Abmayr Germany
J Holmquist Sweden
Tokiya Abe relative to Emmanouil Athanasiadis Greece Emmanouil Athanasiadis's profile →
Citations per field
00.5×2×3×3.7×
Emmanouil Athanasiadis · 1×
Citations per year

Countries citing papers authored by Tokiya Abe

Since Specialization
Citations

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

Fields of papers citing papers by Tokiya Abe

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 50 papers — load more, or switch the sort, to bring in the rest.

#Work
1 202075
2 201974
3 201747
4 201641
5 201334
6 200834
7 200533
8 201229
9 200527
10 200524
11 201424
12 201823
13 201323
14 201822
15 199618
16 201216
17 201914
18 201611
19 201511
20 201611

About Tokiya Abe

Tokiya Abe is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging, Biophysics and Oncology, having authored 50 papers that have together received 681 indexed citations. Recurring topics across this work include AI in cancer detection (25 papers), Digital Imaging for Blood Diseases (16 papers), Image Retrieval and Classification Techniques (7 papers), Cell Image Analysis Techniques (6 papers), Medical Image Segmentation Techniques (4 papers), Radiomics and Machine Learning in Medical Imaging (4 papers), Liver Disease Diagnosis and Treatment (3 papers) and Image Processing Techniques and Applications (3 papers). The work is most often cited by research in Biophysics (96 citations), Hepatology (88 citations), Computer Vision and Pattern Recognition (149 citations), Oncology (146 citations) and Artificial Intelligence (168 citations). Tokiya Abe has collaborated with scholars based in Japan, United States and Russia. Frequent co-authors include Michiie Sakamoto, Masahiro Yamaguchi, Akinori Hashiguchi, Nagaaki Ohyama, Yukako Yagi, Yohei Masugi, Yuri Murakami, Pinky A. Bautista, Minoru Kitago and Akihisa Ueno. Their work appears in journals such as Hepatology Research, Journal of Pathology Informatics, Computerized Medical Imaging and Graphics, Analytical Cellular Pathology and Pathology International.

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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