Fredrik Löfman

673 total citations · 1 hit paper
9 papers, 422 citations indexed

About

Fredrik Löfman is a scholar working on Radiology, Nuclear Medicine and Imaging, Radiation and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Fredrik Löfman has authored 9 papers receiving a total of 422 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Radiology, Nuclear Medicine and Imaging, 6 papers in Radiation and 3 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Fredrik Löfman's work include Radiomics and Machine Learning in Medical Imaging (7 papers), Advanced Radiotherapy Techniques (6 papers) and Digital Radiography and Breast Imaging (2 papers). Fredrik Löfman is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (7 papers), Advanced Radiotherapy Techniques (6 papers) and Digital Radiography and Breast Imaging (2 papers). Fredrik Löfman collaborates with scholars based in Sweden, United States and Belgium. Fredrik Löfman's co-authors include Kevin Souris, Edmond Sterpin, Dan Nguyen, Liesbeth Vandewinckele, Gilmer Valdés, Siri Willems, John A. Lee, Ana María Barragán Montero, Mats Holmström and Benoît Macq and has published in prestigious journals such as International Journal of Radiation Oncology*Biology*Physics, Physics in Medicine and Biology and Radiotherapy and Oncology.

In The Last Decade

Fredrik Löfman

8 papers receiving 405 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.

Artificial intelligence and machine learning for medical imaging: A technology review

2021 236 citations Ana María Barragán Montero, Umair Javaid et al. Physica Medica profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Fredrik Löfman Sweden	6	264	119	115	81	73	9	422
Steven Michiels Belgium	8	242 0.9×	188 1.6×	87 0.8×	91 1.1×	154 2.1×	16	443
Liesbeth Vandewinckele Belgium	7	398 1.5×	250 2.1×	139 1.2×	129 1.6×	112 1.5×	11	593
Siri Willems Belgium	11	342 1.3×	185 1.6×	158 1.4×	107 1.3×	97 1.3×	14	616
Shujun Liang China	9	309 1.2×	75 0.6×	182 1.6×	72 0.9×	44 0.6×	20	467
Sunan Cui United States	11	356 1.3×	139 1.2×	158 1.4×	103 1.3×	133 1.8×	18	574
Umair Javaid Belgium	6	149 0.6×	40 0.3×	86 0.7×	55 0.7×	50 0.7×	11	287
Reza Reiazi Iran	13	298 1.1×	43 0.4×	111 1.0×	83 1.0×	97 1.3×	36	399
Siu Ki Yu Hong Kong	11	380 1.4×	127 1.1×	120 1.0×	62 0.8×	149 2.0×	40	471
Carter Kolbeck Canada	7	208 0.8×	182 1.5×	64 0.6×	80 1.0×	93 1.3×	15	351
Yesenia Gonzalez United States	10	327 1.2×	255 2.1×	66 0.6×	133 1.6×	116 1.6×	28	491

Countries citing papers authored by Fredrik Löfman

Since Specialization

Citations

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

Fields of papers citing papers by Fredrik Löfman

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Fredrik Löfman

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

All Works

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9 of 9 papers shown

1.

Chen, Xinru, Yao Zhao, Skylar Gay, et al.. (2024). Comparison of Vendor-Pretrained and Custom-Trained Deep Learning Segmentation Models for Head-and-Neck, Breast, and Prostate Cancers. Diagnostics. 14(24). 2851–2851.

2.

Löfman, Fredrik, et al.. (2024). Clinical implementation of deep learning robust IMPT planning in oropharyngeal cancer patients: A blinded clinical study. Radiotherapy and Oncology. 200. 110522–110522. 2 indexed citations

3.

Montero, Ana María Barragán, Adrien Bibal, Gilmer Valdés, et al.. (2022). Towards a safe and efficient clinical implementation of machine learning in radiation oncology by exploring model interpretability, explainability and data-model dependency. Physics in Medicine and Biology. 67(11). 11TR01–11TR01. 48 indexed citations

4.

Claessens, Michaël, Verdi Vanreusel, Fredrik Löfman, et al.. (2022). Machine learning-based detection of aberrant deep learning segmentations of target and organs at risk for prostate radiotherapy using a secondary segmentation algorithm. Physics in Medicine and Biology. 67(11). 115014–115014. 20 indexed citations

5.

Montero, Ana María Barragán, Umair Javaid, Gilmer Valdés, et al.. (2021). Artificial intelligence and machine learning for medical imaging: A technology review. Physica Medica. 83. 242–256. 236 indexed citations breakdown →

6.

Shusharina, Nadya, et al.. (2020). Automated delineation of the clinical target volume using anatomically constrained 3D expansion of the gross tumor volume. Radiotherapy and Oncology. 146. 37–43. 34 indexed citations

7.

Rigaud, B., Brian Anderson, Zhiqian Yu, et al.. (2020). Automatic Segmentation Using Deep Learning to Enable Online Dose Optimization During Adaptive Radiation Therapy of Cervical Cancer. International Journal of Radiation Oncology*Biology*Physics. 109(4). 1096–1110. 72 indexed citations

8.

Mercier, Carole, Michaël Claessens, Charlotte Billiet, et al.. (2020). Stereotactic Ablative Radiation Therapy to All Lesions in Patients With Oligometastatic Cancers: A Phase 1 Dose-Escalation Trial. International Journal of Radiation Oncology*Biology*Physics. 109(5). 1195–1205. 9 indexed citations

9.

Rigaud, B., Brian Anderson, Guillaume Cazoulat, et al.. (2020). Automatic Segmentation Using Deep Learning for Online Dose Optimization During Adaptive Radiotherapy of Cervical Cancer. International Journal of Radiation Oncology*Biology*Physics. 108(3). e458–e458. 1 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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