Gen Iinuma

96 total papers · 968 total citations
42 papers, 690 citations indexed

About

Gen Iinuma is a scholar working on Oncology, Surgery and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Gen Iinuma has authored 42 papers receiving a total of 690 indexed citations (citations by other indexed papers that have themselves been cited), including 24 papers in Oncology, 18 papers in Surgery and 16 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Gen Iinuma's work include Colorectal Cancer Screening and Detection (17 papers), Colorectal Cancer Surgical Treatments (12 papers) and Radiomics and Machine Learning in Medical Imaging (10 papers). Gen Iinuma is often cited by papers focused on Colorectal Cancer Screening and Detection (17 papers), Colorectal Cancer Surgical Treatments (12 papers) and Radiomics and Machine Learning in Medical Imaging (10 papers). Gen Iinuma collaborates with scholars based in Japan, United States and United Kingdom. Gen Iinuma's co-authors include Takayuki Akasu, Noriyuki Moriyama, Yukio Muramatsu, Yutaka Saito, Yasuo Ohashi, Daizo Saito, Masaaki Iigo, Tadao Kakizoe, Takahiro Kozu and Hiroyuki Tsuda and has published in prestigious journals such as Journal of Clinical Oncology, Radiology and International Journal of Molecular Sciences.

In The Last Decade

Gen Iinuma

40 papers receiving 666 citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author						Papers	Cites
Gen Iinuma	303	257	182	157	130	42	690
Jona Stadler	249 0.8×	154 0.6×	91 0.5×	78 0.5×	37 0.3×	25	706
Hassan K. Awwad	143 0.5×	311 1.2×	164 0.9×	107 0.7×	65 0.5×	46	791
Naoki Morimoto	113 0.4×	119 0.5×	198 1.1×	107 0.7×	29 0.2×	61	701
Hongfa Zhu	201 0.7×	189 0.7×	118 0.6×	144 0.9×	13 0.1×	42	685
Eugenio Ciancia	125 0.4×	153 0.6×	117 0.6×	27 0.2×	53 0.4×	39	608
Yoshihiro Sasaki	205 0.7×	280 1.1×	183 1.0×	94 0.6×	15 0.1×	53	635
Karel Kithier	102 0.3×	172 0.7×	85 0.5×	43 0.3×	23 0.2×	41	778
Toshifumi Saito	43 0.1×	228 0.9×	144 0.8×	40 0.3×	148 1.1×	36	578
N. Ackerman	116 0.4×	249 1.0×	164 0.9×	119 0.8×	10 0.1×	49	710
Tomoaki Takata	52 0.2×	116 0.5×	160 0.9×	73 0.5×	47 0.4×	70	657

Countries citing papers authored by Gen Iinuma

Since Specialization

Citations

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

Fields of papers citing papers by Gen Iinuma

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Gen Iinuma

This figure shows the co-authorship network connecting the top 25 collaborators of Gen Iinuma. A scholar is included among the top collaborators of Gen Iinuma based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Gen Iinuma. Gen Iinuma is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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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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