Fahdi Kanavati

1.4k total citations · 1 hit paper
20 papers, 794 citations indexed

About

Fahdi Kanavati is a scholar working on Artificial Intelligence, Pulmonary and Respiratory Medicine and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Fahdi Kanavati has authored 20 papers receiving a total of 794 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Artificial Intelligence, 10 papers in Pulmonary and Respiratory Medicine and 10 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Fahdi Kanavati's work include AI in cancer detection (14 papers), Radiomics and Machine Learning in Medical Imaging (10 papers) and Colorectal Cancer Screening and Detection (8 papers). Fahdi Kanavati is often cited by papers focused on AI in cancer detection (14 papers), Radiomics and Machine Learning in Medical Imaging (10 papers) and Colorectal Cancer Screening and Detection (8 papers). Fahdi Kanavati collaborates with scholars based in Japan, United Kingdom and United States. Fahdi Kanavati's co-authors include Masayuki Tsuneki, Osamu Iizuka, Kei Kato, Koji Arihiro, Seiya Momosaki, Koji Yamazaki, Sadanori Takeo, Gouji Toyokawa, Fumihiro Shoji and Yuka Kozuma and has published in prestigious journals such as Nature Communications, PLoS ONE and American Journal of Respiratory and Critical Care Medicine.

In The Last Decade

Fahdi Kanavati

19 papers receiving 761 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Deep Learning Models for Histopathological Classification of Gastric and Colonic Epithelial Tumours

2020 271 citations Osamu Iizuka, Fahdi Kanavati et al. Scientific Reports profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Fahdi Kanavati Japan	11	504	500	218	168	122	20	794
Germán Corredor United States	13	524 1.0×	299 0.6×	348 1.6×	293 1.7×	70 0.6×	57	908
Xinming Zhao China	17	1.0k 2.0×	213 0.4×	252 1.2×	248 1.5×	92 0.8×	51	1.3k
Maxine Tan United States	20	830 1.6×	622 1.2×	144 0.7×	555 3.3×	141 1.2×	44	1.1k
Hans Pinckaers Netherlands	12	477 0.9×	608 1.2×	150 0.7×	217 1.3×	183 1.5×	20	899
José E. Velázquez Vega United States	7	414 0.8×	403 0.8×	120 0.6×	114 0.7×	77 0.6×	15	821
Anurag Vaidya United States	8	407 0.8×	480 1.0×	104 0.5×	83 0.5×	117 1.0×	10	865
Sujata V. Ghate United States	17	521 1.0×	402 0.8×	137 0.6×	253 1.5×	45 0.4×	38	858
Si‐Wa Chan Taiwan	15	530 1.1×	369 0.7×	96 0.4×	296 1.8×	80 0.7×	44	879
Zeyan Xu China	13	440 0.9×	301 0.6×	180 0.8×	91 0.5×	149 1.2×	42	758
Jeremias Krause Germany	3	489 1.0×	466 0.9×	305 1.4×	106 0.6×	99 0.8×	8	868

Countries citing papers authored by Fahdi Kanavati

Since Specialization

Citations

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

Fields of papers citing papers by Fahdi Kanavati

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Fahdi Kanavati

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

All Works

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20 of 20 papers shown

Thillai, Muhunthan, Justin M. Oldham, Alessandro Ruggiero, et al.. (2024). Deep Learning–based Segmentation of Computed Tomography Scans Predicts Disease Progression and Mortality in Idiopathic Pulmonary Fibrosis. American Journal of Respiratory and Critical Care Medicine. 210(4). 465–472. 18 indexed citations

Thillai, Muhunthan, Alessandro Ruggiero, Fahdi Kanavati, et al.. (2024). Deep Learning-based Segmentation of CT Scans Predicts Disease Progression and Mortality in IPF. A5102–A5102.

Abe, Makoto, Fahdi Kanavati, & Masayuki Tsuneki. (2024). Evaluation of a Deep Learning Model for Metastatic Squamous Cell Carcinoma Prediction From Whole Slide Images. Archives of Pathology & Laboratory Medicine. 148(12). 1344–1351. 1 indexed citations

Tsuneki, Masayuki, Makoto Abe, Shin Ichihara, & Fahdi Kanavati. (2023). Inference of core needle biopsy whole slide images requiring definitive therapy for prostate cancer. BMC Cancer. 23(1). 11–11. 4 indexed citations

Tsuneki, Masayuki & Fahdi Kanavati. (2022). Weakly Supervised Learning for Poorly Differentiated Adenocarcinoma Classification in GastricEndoscopic Submucosal Dissection Whole Slide Images. Technology in Cancer Research & Treatment. 21. 2213884562–2213884562. 7 indexed citations

Kanavati, Fahdi, et al.. (2022). A Deep Learning Model for Cervical Cancer Screening on Liquid-Based Cytology Specimens in Whole Slide Images. Cancers. 14(5). 1159–1159. 45 indexed citations

Tsuneki, Masayuki & Fahdi Kanavati. (2022). Weakly supervised learning for multi-organ adenocarcinoma classification in whole slide images. PLoS ONE. 17(11). e0275378–e0275378. 7 indexed citations

Kanavati, Fahdi, Shin Ichihara, & Masayuki Tsuneki. (2022). A deep learning model for breast ductal carcinoma in situ classification in whole slide images. Archiv für Pathologische Anatomie und Physiologie und für Klinische Medicin. 480(5). 1009–1022. 18 indexed citations

Islam, Syed, et al.. (2022). Fully automated deep-learning section-based muscle segmentation from CT images for sarcopenia assessment. Clinical Radiology. 77(5). e363–e371. 13 indexed citations

10.

Tsuneki, Masayuki, Makoto Abe, & Fahdi Kanavati. (2022). Deep Learning-Based Screening of Urothelial Carcinoma in Whole Slide Images of Liquid-Based Cytology Urine Specimens. Cancers. 15(1). 226–226. 10 indexed citations

11.

Tsuneki, Masayuki, Makoto Abe, & Fahdi Kanavati. (2022). A Deep Learning Model for Prostate Adenocarcinoma Classification in Needle Biopsy Whole-Slide Images Using Transfer Learning. Diagnostics. 12(3). 768–768. 22 indexed citations

12.

Tsuneki, Masayuki, Makoto Abe, & Fahdi Kanavati. (2022). Transfer Learning for Adenocarcinoma Classifications in the Transurethral Resection of Prostate Whole-Slide Images. Cancers. 14(19). 4744–4744. 7 indexed citations

13.

Kanavati, Fahdi, Gouji Toyokawa, Seiya Momosaki, et al.. (2021). A deep learning model for the classification of indeterminate lung carcinoma in biopsy whole slide images. Scientific Reports. 11(1). 8110–8110. 39 indexed citations

14.

Roberts, Michael, Tom M. McLellan, Evan H. Morgan, et al.. (2021). Late Breaking Abstract - Fully automated airway measurement correlates with radiological disease progression in Idiopathic Pulmonary Fibrosis. OA3951–OA3951. 3 indexed citations

15.

Tsuneki, Masayuki & Fahdi Kanavati. (2021). Deep Learning Models for Poorly Differentiated Colorectal Adenocarcinoma Classification in Whole Slide Images Using Transfer Learning. Diagnostics. 11(11). 2074–2074. 17 indexed citations

16.

Kanavati, Fahdi & Masayuki Tsuneki. (2021). Breast Invasive Ductal Carcinoma Classification on Whole Slide Images with Weakly-Supervised and Transfer Learning. Cancers. 13(21). 5368–5368. 19 indexed citations

17.

Iizuka, Osamu, et al.. (2020). Deep Learning Models for Histopathological Classification of Gastric and Colonic Epithelial Tumours. Scientific Reports. 10(1). 1504–1504. 271 indexed citations breakdown →

18.

Kanavati, Fahdi, Gouji Toyokawa, Seiya Momosaki, et al.. (2020). Weakly-supervised learning for lung carcinoma classification using deep learning. Scientific Reports. 10(1). 9297–9297. 155 indexed citations

19.

Lu, Haonan, Mubarik Arshad, Andrew Thornton, et al.. (2019). A mathematical-descriptor of tumor-mesoscopic-structure from computed-tomography images annotates prognostic- and molecular-phenotypes of epithelial ovarian cancer. Nature Communications. 10(1). 764–764. 128 indexed citations

20.

Kanavati, Fahdi, Tong Tong, Kazunari Misawa, et al.. (2016). Supervoxel classification forests for estimating pairwise image correspondences. Pattern Recognition. 63. 561–569. 10 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.

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