David Tellez

4.6k total citations · 1 hit paper
9 papers, 592 citations indexed

About

David Tellez is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging and Computer Vision and Pattern Recognition. According to data from OpenAlex, David Tellez has authored 9 papers receiving a total of 592 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Artificial Intelligence, 5 papers in Radiology, Nuclear Medicine and Imaging and 2 papers in Computer Vision and Pattern Recognition. Recurrent topics in David Tellez's work include AI in cancer detection (7 papers), Radiomics and Machine Learning in Medical Imaging (4 papers) and Breast Cancer Treatment Studies (2 papers). David Tellez is often cited by papers focused on AI in cancer detection (7 papers), Radiomics and Machine Learning in Medical Imaging (4 papers) and Breast Cancer Treatment Studies (2 papers). David Tellez collaborates with scholars based in Netherlands, Sweden and Belgium. David Tellez's co-authors include Francesco Ciompi, Jeroen van der Laak, Geert Litjens, Péter Bándi, Wouter Bulten, John‐Melle Bokhorst, Maschenka Balkenhol, P C Clahsen, Willem Vreuls and Peter Bult and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Medical Image Analysis and Laboratory Investigation.

In The Last Decade

David Tellez

9 papers receiving 580 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.

Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology

2019 317 citations David Tellez, Geert Litjens et al. Medical Image Analysis profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
David Tellez Netherlands	7	480	310	243	95	84	9	592
Oscar Geessink Netherlands	6	428 0.9×	320 1.0×	195 0.8×	132 1.4×	102 1.2×	7	567
John‐Melle Bokhorst Netherlands	13	402 0.8×	282 0.9×	201 0.8×	192 2.0×	74 0.9×	22	617
Nikolas Stathonikos Netherlands	14	508 1.1×	328 1.1×	163 0.7×	124 1.3×	108 1.3×	32	687
Monjoy Saha India	10	371 0.8×	269 0.9×	172 0.7×	73 0.8×	91 1.1×	18	559
Péter Bándi Netherlands	8	617 1.3×	421 1.4×	292 1.2×	116 1.2×	125 1.5×	15	779
Ozan Ciga Canada	4	387 0.8×	250 0.8×	193 0.8×	80 0.8×	65 0.8×	4	484
Eduardo Castro Portugal	4	601 1.3×	458 1.5×	300 1.2×	76 0.8×	60 0.7×	10	728
Chetan L. Srinidhi India	6	358 0.7×	368 1.2×	266 1.1×	75 0.8×	62 0.7×	7	602
Żaneta Świderska-Chadaj Poland	13	360 0.8×	273 0.9×	141 0.6×	103 1.1×	83 1.0×	30	582

Countries citing papers authored by David Tellez

Since Specialization

Citations

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

Fields of papers citing papers by David Tellez

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of David Tellez

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

9 of 9 papers shown

1.

Höppener, Diederik J., David Tellez, Pieter M. H. Nierop, et al.. (2024). Classifying histopathological growth patterns for resected colorectal liver metastasis with a deep learning analysis. BJS Open. 8(6). 5 indexed citations

2.

Tellez, David, et al.. (2021). Neural image compression for non-small cell lung cancer subtype classification in H&E stained whole-slide images. 1–1. 6 indexed citations

3.

Balkenhol, Maschenka, David Tellez, Willem Vreuls, et al.. (2019). Deep learning assisted mitotic counting for breast cancer. Laboratory Investigation. 99(11). 1596–1606. 73 indexed citations

4.

Balkenhol, Maschenka, Peter Bult, David Tellez, et al.. (2019). Deep learning and manual assessment show that the absolute mitotic count does not contain prognostic information in triple negative breast cancer. Cellular Oncology. 42(4). 555–569. 17 indexed citations

5.

Tellez, David, Geert Litjens, Jeroen van der Laak, & Francesco Ciompi. (2019). Neural Image Compression for Gigapixel Histopathology Image Analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence. 43(2). 567–578. 146 indexed citations

6.

Tellez, David, Geert Litjens, Péter Bándi, et al.. (2019). Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology. Medical Image Analysis. 58. 101544–101544. 317 indexed citations breakdown →

7.

Tellez, David, Jeroen van der Laak, & Francesco Ciompi. (2018). Gigapixel Whole-Slide Image Classification Using Unsupervised Image Compression And Contrastive Training. 5 indexed citations

8.

Balkenhol, Maschenka, Nico Karssemeijer, Geert Litjens, et al.. (2018). H&E stain augmentation improves generalization of convolutional networks for histopathological mitosis detection. 34–34. 16 indexed citations

9.

Nagy, Tamás, et al.. (2014). Predicting arousal with machine learning of EEG signals. 137–140. 7 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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