Federico Pollastri

466 total citations
15 papers, 255 citations indexed

About

Federico Pollastri is a scholar working on Oncology, Artificial Intelligence and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Federico Pollastri has authored 15 papers receiving a total of 255 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Oncology, 5 papers in Artificial Intelligence and 4 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Federico Pollastri's work include AI in cancer detection (5 papers), Dental Radiography and Imaging (4 papers) and Cutaneous Melanoma Detection and Management (4 papers). Federico Pollastri is often cited by papers focused on AI in cancer detection (5 papers), Dental Radiography and Imaging (4 papers) and Cutaneous Melanoma Detection and Management (4 papers). Federico Pollastri collaborates with scholars based in Italy, Spain and Germany. Federico Pollastri's co-authors include Costantino Grana, Federico Bolelli, Roberto Paredes, Stefano Allegretti, Alexandre Anesi, Mattia Di Bartolomeo, Giulia Ligabue, Riccardo Magistroni, Silvia Giovanella and Marco Leonelli and has published in prestigious journals such as Cancer Research, IEEE Access and Clinical Journal of the American Society of Nephrology.

In The Last Decade

Federico Pollastri

15 papers receiving 255 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Federico Pollastri Italy	10	95	74	65	64	56	15	255
C. L. Benhamou France	11	45 0.5×	79 1.1×	80 1.2×	80 1.3×	141 2.5×	19	699
Nicola Altini Italy	12	172 1.8×	18 0.2×	37 0.6×	102 1.6×	50 0.9×	26	343
Sari Suortamo United Kingdom	3	134 1.4×	43 0.6×	65 1.0×	29 0.5×	9 0.2×	4	255
Akshat Gotra Canada	10	60 0.6×	16 0.2×	25 0.4×	87 1.4×	45 0.8×	13	324
Alanna Vial Australia	5	117 1.2×	13 0.2×	33 0.5×	44 0.7×	87 1.6×	8	440
Carlos F. Villamil Canada	8	152 1.6×	7 0.1×	52 0.8×	40 0.6×	36 0.6×	19	310
Viviana Benfante Italy	13	66 0.7×	21 0.3×	30 0.5×	64 1.0×	92 1.6×	21	390
Haiyan Du China	10	36 0.4×	74 1.0×	20 0.3×	17 0.3×	75 1.3×	16	284
Bingjiang Qiu China	12	149 1.6×	114 1.5×	36 0.6×	104 1.6×	120 2.1×	23	484
Yuting Guo China	9	107 1.1×	39 0.5×	102 1.6×	32 0.5×	47 0.8×	25	297

Countries citing papers authored by Federico Pollastri

Since Specialization

Citations

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

Fields of papers citing papers by Federico Pollastri

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Federico Pollastri

This figure shows the co-authorship network connecting the top 25 collaborators of Federico Pollastri. A scholar is included among the top collaborators of Federico Pollastri 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 Federico Pollastri. Federico Pollastri 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:

15 of 15 papers shown

Schick, Markus, Thomas Kunzke, Federico Pollastri, et al.. (2024). Abstract 2492: Enhanced patient selection for anti-PD-L1 treatment in metastatic NSCLC with quantitative continuous scoring of PD-L1. Cancer Research. 84(6_Supplement). 2492–2492. 1 indexed citations

Bartolomeo, Mattia Di, Federico Bolelli, Stefano Allegretti, et al.. (2023). Inferior Alveolar Canal Automatic Detection with Deep Learning CNNs on CBCTs: Development of a Novel Model and Release of Open-Source Dataset and Algorithm. Applied Sciences. 13(5). 3271–3271. 9 indexed citations

Testa, Francesca, Francesco Fontana, Federico Pollastri, et al.. (2022). Automated Prediction of Kidney Failure in IgA Nephropathy with Deep Learning from Biopsy Images. Clinical Journal of the American Society of Nephrology. 17(9). 1316–1324. 13 indexed citations

Schick, Markus, Thomas Kunzke, Thomas Padel, et al.. (2022). 583 Quantitative computational assessment of PD-L1 enables robust patient selection for biomarker-informed anti-PD-L1 treatment of NSCLC patients. Regular and Young Investigator Award Abstracts. A610–A610. 1 indexed citations

Allegretti, Stefano, Federico Bolelli, Mattia Di Bartolomeo, et al.. (2022). Deep Segmentation of the Mandibular Canal: A New 3D Annotated Dataset of CBCT Volumes. IEEE Access. 10. 11500–11510. 36 indexed citations

Allegretti, Stefano, et al.. (2022). Improving Segmentation of the Inferior Alveolar Nerve through Deep Label Propagation. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 21105–21114. 14 indexed citations

Pollastri, Federico, et al.. (2022). Long-Range 3D Self-Attention for MRI Prostate Segmentation. 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI). 1–5. 4 indexed citations

Pollastri, Federico, Federico Bolelli, Giulia Ligabue, et al.. (2021). Confidence Calibration for Deep Renal Biopsy Immunofluorescence Image Classification. IRIS UNIMORE (University of Modena and Reggio Emilia). 1298–1305. 9 indexed citations

Allegretti, Stefano, et al.. (2021). Supporting Skin Lesion Diagnosis with Content-Based Image Retrieval. IRIS UNIMORE (University of Modena and Reggio Emilia). 8053–8060. 19 indexed citations

10.

Bolelli, Federico, et al.. (2021). A Cone Beam Computed Tomography Annotation Tool for Automatic Detection of the Inferior Alveolar Nerve Canal. IRIS UNIMORE (University of Modena and Reggio Emilia). 724–731. 12 indexed citations

11.

Pollastri, Federico, et al.. (2021). A deep analysis on high‐resolution dermoscopic image classification. IET Computer Vision. 15(7). 514–526. 9 indexed citations

12.

Ligabue, Giulia, Federico Pollastri, Francesco Fontana, et al.. (2020). Evaluation of the Classification Accuracy of the Kidney Biopsy Direct Immunofluorescence through Convolutional Neural Networks. Clinical Journal of the American Society of Nephrology. 15(10). 1445–1454. 37 indexed citations

13.

Pollastri, Federico, Federico Bolelli, Roberto Paredes, & Costantino Grana. (2019). Augmenting data with GANs to segment melanoma skin lesions. Multimedia Tools and Applications. 79(21-22). 15575–15592. 60 indexed citations

14.

Pollastri, Federico, et al.. (2018). Improving Skin Lesion Segmentation with Generative Adversarial Networks. IRIS UNIMORE (University of Modena and Reggio Emilia). 442–443. 20 indexed citations

15.

Bolelli, Federico, Lorenzo Baraldi, Federico Pollastri, & Costantino Grana. (2018). A Hierarchical Quasi-Recurrent approach to Video Captioning. IRIS UNIMORE (University of Modena and Reggio Emilia). 162–167. 11 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