Akitoshi Shimazaki

875 total citations
13 papers, 625 citations indexed

About

Akitoshi Shimazaki is a scholar working on Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine and Epidemiology. According to data from OpenAlex, Akitoshi Shimazaki has authored 13 papers receiving a total of 625 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Radiology, Nuclear Medicine and Imaging, 6 papers in Pulmonary and Respiratory Medicine and 5 papers in Epidemiology. Recurrent topics in Akitoshi Shimazaki's work include Radiomics and Machine Learning in Medical Imaging (6 papers), AI in cancer detection (5 papers) and COVID-19 diagnosis using AI (3 papers). Akitoshi Shimazaki is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (6 papers), AI in cancer detection (5 papers) and COVID-19 diagnosis using AI (3 papers). Akitoshi Shimazaki collaborates with scholars based in Japan. Akitoshi Shimazaki's co-authors include Yukio Miki, Daiju Ueda, Akira Yamamoto, Norihisa Nishimura, Yoshiaki Azuma, Kentaro Inui, Yoshiki Yamano, Yuki Shimahara, Takashi Honjo and Yutaka Katayama and has published in prestigious journals such as PLoS ONE, Scientific Reports and Radiology.

In The Last Decade

Akitoshi Shimazaki

13 papers receiving 604 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Akitoshi Shimazaki Japan	12	302	202	164	135	105	13	625
Thomas Weikert Switzerland	16	427 1.4×	173 0.9×	144 0.9×	154 1.1×	103 1.0×	38	759
C Martinenghi Italy	13	241 0.8×	99 0.5×	69 0.4×	34 0.3×	55 0.5×	33	499
Jingxu Xu China	13	346 1.1×	163 0.8×	103 0.6×	36 0.3×	131 1.2×	44	521
Alexander Bilbily Canada	6	313 1.0×	90 0.4×	104 0.6×	31 0.2×	96 0.9×	14	527
Christian Blüthgen Switzerland	15	320 1.1×	180 0.9×	156 1.0×	43 0.3×	34 0.3×	38	565
Katarzyna Gruszczyńska Poland	14	203 0.7×	103 0.5×	60 0.4×	56 0.4×	54 0.5×	63	668
Nathaniel Swinburne United States	10	223 0.7×	114 0.6×	70 0.4×	57 0.4×	113 1.1×	18	512
Jochen von Spiczak Switzerland	18	726 2.4×	183 0.9×	416 2.5×	76 0.6×	37 0.4×	39	1.0k
Fayu Liu China	14	143 0.5×	84 0.4×	45 0.3×	51 0.4×	117 1.1×	34	671
Victor А. Gombolevskiy Russia	11	212 0.7×	77 0.4×	69 0.4×	23 0.2×	54 0.5×	49	429

Countries citing papers authored by Akitoshi Shimazaki

Since Specialization

Citations

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

Fields of papers citing papers by Akitoshi Shimazaki

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Akitoshi Shimazaki

This figure shows the co-authorship network connecting the top 25 collaborators of Akitoshi Shimazaki. A scholar is included among the top collaborators of Akitoshi Shimazaki 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 Akitoshi Shimazaki. Akitoshi Shimazaki is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

13 of 13 papers shown

Honjo, Takashi, Daiju Ueda, Yutaka Katayama, et al.. (2022). Visual and quantitative evaluation of microcalcifications in mammograms with deep learning-based super-resolution. European Journal of Radiology. 154. 110433–110433. 6 indexed citations

Ueda, Daiju, Akira Yamamoto, Naoyoshi Onoda, et al.. (2022). Development and validation of a deep learning model for detection of breast cancers in mammography from multi-institutional datasets. PLoS ONE. 17(3). e0265751–e0265751. 17 indexed citations

Shimazaki, Akitoshi, Daiju Ueda, Akira Yamamoto, et al.. (2022). Deep learning-based algorithm for lung cancer detection on chest radiographs using the segmentation method. Scientific Reports. 12(1). 727–727. 90 indexed citations

Ueda, Daiju, Shoichi Ehara, Akira Yamamoto, et al.. (2022). Development and Validation of Artificial Intelligence–based Method for Diagnosis of Mitral Regurgitation from Chest Radiographs. Radiology Artificial Intelligence. 4(2). e210221–e210221. 13 indexed citations

Ueda, Daiju, Akira Yamamoto, Tsutomu Takashima, et al.. (2021). Training, Validation, and Test of Deep Learning Models for Classification of Receptor Expressions in Breast Cancers From Mammograms. JCO Precision Oncology. 5(5). 543–551. 15 indexed citations

Ueda, Daiju, Akira Yamamoto, Akitoshi Shimazaki, et al.. (2021). Artificial intelligence-supported lung cancer detection by multi-institutional readers with multi-vendor chest radiographs: a retrospective clinical validation study. BMC Cancer. 21(1). 1120–1120. 33 indexed citations

Ueda, Daiju, Yutaka Katayama, Akira Yamamoto, et al.. (2021). Deep Learning–based Angiogram Generation Model for Cerebral Angiography without Misregistration Artifacts. Radiology. 299(3). 675–681. 25 indexed citations

Ueda, Daiju, Akira Yamamoto, Shoichi Ehara, et al.. (2021). Artificial intelligence-based detection of aortic stenosis from chest radiographs. European Heart Journal - Digital Health. 3(1). 20–28. 20 indexed citations

Ueda, Daiju, Akira Yamamoto, Tsutomu Takashima, et al.. (2020). Visualizing “featureless” regions on mammograms classified as invasive ductal carcinomas by a deep learning algorithm: the promise of AI support in radiology. Japanese Journal of Radiology. 39(4). 333–340. 13 indexed citations

10.

Ueda, Daiju, Akitoshi Shimazaki, & Yukio Miki. (2018). Technical and clinical overview of deep learning in radiology. Japanese Journal of Radiology. 37(1). 15–33. 69 indexed citations

11.

Ueda, Daiju, Akira Yamamoto, Taro Shimono, et al.. (2018). Deep Learning for MR Angiography: Automated Detection of Cerebral Aneurysms. Radiology. 290(1). 187–194. 147 indexed citations

12.

Shimazaki, Akitoshi, Kentaro Inui, Yoshiaki Azuma, Norihisa Nishimura, & Yoshiki Yamano. (2000). Low-intensity pulsed ultrasound accelerates bone maturation in distraction osteogenesis in rabbits. Journal of Bone and Joint Surgery - British Volume. 82(7). 1077–1082. 136 indexed citations

13.

Shimazaki, Akitoshi, et al.. (2000). Low-intensity pulsed ultrasound accelerates bone maturation in distraction osteogenesis in rabbits. Journal of Bone and Joint Surgery - British Volume. 82-B(7). 1077–1082. 41 indexed citations

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.

Explore authors with similar magnitude of impact