Eric Cosatto

3.3k total citations
49 papers, 2.3k citations indexed

About

Eric Cosatto is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Signal Processing. According to data from OpenAlex, Eric Cosatto has authored 49 papers receiving a total of 2.3k indexed citations (citations by other indexed papers that have themselves been cited), including 30 papers in Computer Vision and Pattern Recognition, 27 papers in Artificial Intelligence and 11 papers in Signal Processing. Recurrent topics in Eric Cosatto's work include AI in cancer detection (17 papers), Speech and Audio Processing (11 papers) and Face recognition and analysis (9 papers). Eric Cosatto is often cited by papers focused on AI in cancer detection (17 papers), Speech and Audio Processing (11 papers) and Face recognition and analysis (9 papers). Eric Cosatto collaborates with scholars based in United States, Japan and Chile. Eric Cosatto's co-authors include Hans Peter Graf, Urs Müller, Beat Flepp, Y. Le Cun, Jan Ben, Gerasimos Potamianos, Léon Bottou, Vladimir Vapnik, Fu Jie Huang and Srimat Chakradhar and has published in prestigious journals such as SHILAP Revista de lepidopterología, Proceedings of the IEEE and Scientific Reports.

In The Last Decade

Eric Cosatto

48 papers receiving 2.1k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Eric Cosatto United States 24 1.2k 1.0k 436 344 253 49 2.3k
Muazzam A. Khan Pakistan 28 697 0.6× 860 0.9× 362 0.8× 171 0.5× 352 1.4× 121 2.4k
Yilong Yin China 31 2.0k 1.7× 1.6k 1.6× 360 0.8× 526 1.5× 86 0.3× 235 3.5k
Mohamed Hammad Egypt 28 494 0.4× 822 0.8× 370 0.8× 463 1.3× 155 0.6× 78 2.8k
Mita Nasipuri India 34 2.8k 2.4× 1.2k 1.1× 307 0.7× 438 1.3× 126 0.5× 305 4.3k
Esa Rahtu Finland 29 2.4k 2.0× 556 0.6× 621 1.4× 285 0.8× 107 0.4× 95 3.5k
Banshidhar Majhi India 31 1.8k 1.5× 1.3k 1.3× 503 1.2× 588 1.7× 217 0.9× 189 3.3k
Mudassar Raza Pakistan 32 1.9k 1.6× 1.1k 1.1× 302 0.7× 783 2.3× 69 0.3× 116 3.4k
Lingqiao Liu Australia 32 2.9k 2.5× 2.4k 2.4× 218 0.5× 256 0.7× 217 0.9× 105 4.7k
Ayman Altameem Saudi Arabia 24 694 0.6× 510 0.5× 103 0.2× 183 0.5× 650 2.6× 90 2.4k
Xiao Wu China 30 2.0k 1.7× 827 0.8× 143 0.3× 118 0.3× 279 1.1× 172 3.4k

Countries citing papers authored by Eric Cosatto

Since Specialization
Citations

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

Fields of papers citing papers by Eric Cosatto

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Eric Cosatto

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

All Works

20 of 20 papers shown
1.
Kiyuna, Tomoharu, Eric Cosatto, Kanako C. Hatanaka, et al.. (2024). Evaluating Cellularity Estimation Methods: Comparing AI Counting with Pathologists’ Visual Estimates. Diagnostics. 14(11). 1115–1115. 2 indexed citations
2.
Saito, Akira, Koji Fujita, Masaharu Kobayashi, et al.. (2023). Development of multiple AI pipelines that predict neoadjuvant chemotherapy response of breast cancer using H&E‐stained tissues. The Journal of Pathology Clinical Research. 9(3). 182–194. 6 indexed citations
3.
Saito, Akira, Takeshi Hashimoto, Naoya Satake, et al.. (2021). Prediction of non-muscle invasive bladder cancer recurrence using machine learning of quantitative nuclear features. Modern Pathology. 35(4). 533–538. 34 indexed citations
4.
Saito, Akira, Hidenori Toyoda, Masaharu Kobayashi, et al.. (2020). Prediction of early recurrence of hepatocellular carcinoma after resection using digital pathology images assessed by machine learning. Modern Pathology. 34(2). 417–425. 72 indexed citations
5.
Yamamoto, Yoichiro, Akira Saito, Ayako Tateishi, et al.. (2017). Quantitative diagnosis of breast tumors by morphometric classification of microenvironmental myoepithelial cells using a machine learning approach. Scientific Reports. 7(1). 46732–46732. 32 indexed citations
6.
Oikawa, Kosuke, Akira Saito, Tomoharu Kiyuna, et al.. (2017). Pathological Diagnosis of Gastric Cancers with a Novel Computerized Analysis System. Journal of Pathology Informatics. 8(1). 5–5. 16 indexed citations
7.
Yoshida, Hiroshi, Taichi Shimazu, Tomoharu Kiyuna, et al.. (2017). Automated histological classification of whole-slide images of gastric biopsy specimens. Gastric Cancer. 21(2). 249–257. 77 indexed citations
8.
Yamada, Masatoshi, Akira Saito, Yoichiro Yamamoto, et al.. (2016). Quantitative nucleic features are effective for discrimination of intraductal proliferative lesions of the breast. Journal of Pathology Informatics. 7(1). 1–1. 12 indexed citations
9.
Saito, Akira, et al.. (2016). A novel method for morphological pleomorphism and heterogeneity quantitative measurement: Named cell feature level co-occurrence matrix. Journal of Pathology Informatics. 7(1). 36–36. 21 indexed citations
10.
Cosatto, Eric, et al.. (2013). Classification of mitotic figures with convolutional neural networks and seeded blob features. Journal of Pathology Informatics. 4(1). 9–9. 148 indexed citations
11.
Saito, Akira, Eric Cosatto, Tomoharu Kiyuna, & Michiie Sakamoto. (2013). Dawn of the digital diagnosis assisting system, can it open a new age for pathology?. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 8676. 867602–867602. 8 indexed citations
12.
Jakkula, Venkata, Srihari Cadambi, Srimat Chakradhar, et al.. (2009). A Massively Parallel Coprocessor for Convolutional Neural Networks. 53–60. 175 indexed citations
13.
Graf, Hans Peter, et al.. (2008). A Massively Parallel Digital Learning Processor. Neural Information Processing Systems. 21. 529–536. 26 indexed citations
14.
Müller, Urs, Jan Ben, Eric Cosatto, Beat Flepp, & Y. Le Cun. (2005). Off-Road Obstacle Avoidance through End-to-End Learning. Neural Information Processing Systems. 18. 739–746. 287 indexed citations
15.
Cosatto, Eric, Hans Peter Graf, Jörn Östermann, & Juergen Schroeter. (2004). From audio-only to audio and video text-to-speech. 90(6). 1084–1095.
16.
Basso, Andrea, et al.. (2002). Virtual light: digitally-generated lighting for video conferencing applications. 2. 1085–1088. 5 indexed citations
17.
Graf, Hans Peter, Eric Cosatto, & Tony Ezzat. (2002). Face analysis for the synthesis of photo-realistic talking heads. 189–194. 23 indexed citations
18.
Cosatto, Eric, Gerasimos Potamianos, & Hans Peter Graf. (2002). Audio-visual unit selection for the synthesis of photo-realistic talking-heads. 2. 619–622. 23 indexed citations
19.
Cosatto, Eric & Hans Peter Graf. (2000). Photo-realistic talking-heads from image samples. IEEE Transactions on Multimedia. 2(3). 152–163. 93 indexed citations
20.
Graf, Hans Peter & Eric Cosatto. (1993). Address Block Location with a Neural Net System. Neural Information Processing Systems. 6. 785–792. 2 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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