Jaakko Sahlsten

468 total citations
15 papers, 230 citations indexed

About

Jaakko Sahlsten is a scholar working on Radiology, Nuclear Medicine and Imaging, Oral Surgery and Biomedical Engineering. According to data from OpenAlex, Jaakko Sahlsten has authored 15 papers receiving a total of 230 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Radiology, Nuclear Medicine and Imaging, 4 papers in Oral Surgery and 4 papers in Biomedical Engineering. Recurrent topics in Jaakko Sahlsten's work include Radiomics and Machine Learning in Medical Imaging (6 papers), Dental Radiography and Imaging (4 papers) and Medical Imaging and Analysis (3 papers). Jaakko Sahlsten is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (6 papers), Dental Radiography and Imaging (4 papers) and Medical Imaging and Analysis (3 papers). Jaakko Sahlsten collaborates with scholars based in Finland, United States and United Kingdom. Jaakko Sahlsten's co-authors include Kimmo Kaski, Joel Jaskari, Jorma Järnstedt, Mohamed A. Naser, Kareem A. Wahid, Clifton D. Fuller, Leo Kärkkäinen, Kustaa Hietala, Simo Särkkä and Theodoros Damoulas and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and Scientific Reports.

In The Last Decade

Jaakko Sahlsten

13 papers receiving 223 citations

Peers

Jaakko Sahlsten

RNMI

Joel Jaskari Finland

Lanzhuju Mei China

Aruna Ramesh United States

Dania Tamimi United States

Tae-Hoon Yong South Korea

Kazumasa Yoshii Japan

Yaser Ali Alhazmi Saudi Arabia

Alina Jacob Germany

Jyotsna Rimal Nepal

Patcharapon Jantana Thailand

Joel Jaskari Finland

Jaakko Sahlsten

104 ×1.0

RNMI

80 ×1.0

29 ×1.0

31 ×1.1

Citations per year, relative to Jaakko Sahlsten Jaakko Sahlsten (= 1×) peers Joel Jaskari

Countries citing papers authored by Jaakko Sahlsten

Since Specialization

Citations

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

Fields of papers citing papers by Jaakko Sahlsten

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jaakko Sahlsten

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

All Works

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15 of 15 papers shown

Sahlsten, Jaakko, et al.. (2025). Head and Neck Tumor Segmentation Using Pre-RT MRI Scans and Cascaded DualUNet. Lecture notes in computer science. 15273. 191–203.

Kangas, Jari, Joel Jaskari, Jaakko Sahlsten, et al.. (2025). Diving, Grabbing and Teleporting: Methods for Medical 3D Image Manipulation in VR. Interacting with Computers. 37(2). 105–119.

Sahlsten, Jaakko, Joel Jaskari, Kareem A. Wahid, et al.. (2024). Application of simultaneous uncertainty quantification and segmentation for oropharyngeal cancer use-case with Bayesian deep learning. SHILAP Revista de lepidopterología. 4(1). 110–110. 6 indexed citations

Sahlsten, Jaakko, et al.. (2024). Deep learning for 3D cephalometric landmarking with heterogeneous multi-center CBCT dataset. PLoS ONE. 19(6). e0305947–e0305947. 4 indexed citations

Jaskari, Joel, Jaakko Sahlsten, Paula Summanen, et al.. (2024). DR-GPT: A large language model for medical report analysis of diabetic retinopathy patients. PLoS ONE. 19(10). e0297706–e0297706. 4 indexed citations

Wahid, Kareem A., Jaakko Sahlsten, Joel Jaskari, et al.. (2023). Harnessing uncertainty in radiotherapy auto-segmentation quality assurance. Physics and Imaging in Radiation Oncology. 29. 100526–100526. 4 indexed citations

Järnstedt, Jorma, et al.. (2023). Reproducibility analysis of automated deep learning based localisation of mandibular canals on a temporal CBCT dataset. Scientific Reports. 13(1). 14159–14159. 4 indexed citations

Sahlsten, Jaakko, Kareem A. Wahid, Enrico Glerean, et al.. (2023). Segmentation stability of human head and neck cancer medical images for radiotherapy applications under de-identification conditions: Benchmarking data sharing and artificial intelligence use-cases. Frontiers in Oncology. 13. 1120392–1120392. 3 indexed citations

Wahid, Kareem A., Brennan Olson, Aaron J. Grossberg, et al.. (2022). Muscle and adipose tissue segmentations at the third cervical vertebral level in patients with head and neck cancer. Scientific Data. 9(1). 470–470. 6 indexed citations

10.

Järnstedt, Jorma, et al.. (2022). Comparison of deep learning segmentation and multigrader-annotated mandibular canals of multicenter CBCT scans. Scientific Reports. 12(1). 18598–18598. 13 indexed citations

11.

Taku, Nicolette, Kareem A. Wahid, Lisanne V. van Dijk, et al.. (2022). Auto-detection and segmentation of involved lymph nodes in HPV-associated oropharyngeal cancer using a convolutional deep learning neural network. Clinical and Translational Radiation Oncology. 36. 47–55. 12 indexed citations

12.

Wahid, Kareem A., Enrico Glerean, Jaakko Sahlsten, et al.. (2022). Artificial Intelligence for Radiation Oncology Applications Using Public Datasets. Seminars in Radiation Oncology. 32(4). 400–414. 16 indexed citations

13.

Naser, Mohamed A., Kareem A. Wahid, Aaron J. Grossberg, et al.. (2022). Deep learning auto-segmentation of cervical skeletal muscle for sarcopenia analysis in patients with head and neck cancer. Frontiers in Oncology. 12. 930432–930432. 12 indexed citations

14.

Jaskari, Joel, Jaakko Sahlsten, Theodoros Damoulas, et al.. (2022). Uncertainty-Aware Deep Learning Methods for Robust Diabetic Retinopathy Classification. IEEE Access. 10. 76669–76681. 30 indexed citations

15.

Jaskari, Joel, et al.. (2020). Deep Learning Method for Mandibular Canal Segmentation in Dental Cone Beam Computed Tomography Volumes. Scientific Reports. 10(1). 5842–5842. 116 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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