Thomas J. Fuchs

8.6k total citations · 2 hit papers
70 papers, 3.8k citations indexed

About

Thomas J. Fuchs is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Thomas J. Fuchs has authored 70 papers receiving a total of 3.8k indexed citations (citations by other indexed papers that have themselves been cited), including 29 papers in Artificial Intelligence, 16 papers in Computer Vision and Pattern Recognition and 16 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Thomas J. Fuchs's work include AI in cancer detection (21 papers), Cell Image Analysis Techniques (12 papers) and Radiomics and Machine Learning in Medical Imaging (11 papers). Thomas J. Fuchs is often cited by papers focused on AI in cancer detection (21 papers), Cell Image Analysis Techniques (12 papers) and Radiomics and Machine Learning in Medical Imaging (11 papers). Thomas J. Fuchs collaborates with scholars based in United States, Switzerland and Germany. Thomas J. Fuchs's co-authors include Gabriele Campanella, David S. Klimstra, Matthew G. Hanna, Victor E. Reuter, Edi Brogi, Joachim M. Buhmann, Luke Geneslaw, Vitor Werneck Krauss Silva, Allen P. Miraflor and Klaus J. Busam and has published in prestigious journals such as Nature Medicine, Nature Communications and Gastroenterology.

In The Last Decade

Thomas J. Fuchs

66 papers receiving 3.7k 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.

Clinical-grade computational pathology using weakly supervised deep learning on whole slide images

2019 1.4k citations Gabriele Campanella, Matthew G. Hanna et al. profile →
DeepPET: A deep encoder–decoder network for directly solving the PET image reconstruction inverse problem

2019 201 citations Gabriele Campanella, Thomas J. Fuchs et al. profile →

Peers

Thomas J. Fuchs

RNMI

CVPR

ONCOL

John Tomaszewski United States

David J. Foran United States

Cheng Lu China

Hannah Gilmore United States

Dong Ni China

Mia K. Markey United States

Chen Li China

Nicolas Coudray United States

Metin N. Gürcan United States

Qingli Li China

John Tomaszewski United States

Thomas J. Fuchs

2.1k ×1.1

1.5k ×1.0

RNMI

1.2k ×1.3

CVPR

379 ×0.6

ONCOL

540 ×0.9

Citations per year, relative to Thomas J. Fuchs Thomas J. Fuchs (= 1×) peers John Tomaszewski

Countries citing papers authored by Thomas J. Fuchs

Since Specialization

Citations

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

Fields of papers citing papers by Thomas J. Fuchs

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Thomas J. Fuchs. 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 Thomas J. Fuchs. The network helps show where Thomas J. Fuchs may publish in the future.

Co-authorship network of co-authors of Thomas J. Fuchs

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

All Works

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20 of 20 papers shown

Khosravi, Pegah, Thomas J. Fuchs, & David Joon Ho. (2025). Artificial Intelligence–Driven Cancer Diagnostics: Enhancing Radiology and Pathology through Reproducibility, Explainability, and Multimodality. Cancer Research. 85(13). 2356–2367. 3 indexed citations

Hirten, Robert, Matteo Danieletto, Kyungwon Lee, et al.. (2025). Physiological Data Collected From Wearable Devices Identify and Predict Inflammatory Bowel Disease Flares. Gastroenterology. 168(5). 939–951.e5. 14 indexed citations

Marra, Antonio, Stefania Morganti, Fresia Pareja, et al.. (2025). Artificial intelligence entering the pathology arena in oncology: current applications and future perspectives. Annals of Oncology. 36(7). 712–725. 16 indexed citations

Fuchs, Thomas J., Jackie Donovan, Samantha Irving, et al.. (2024). EPS7.07 Exploring the relationship of compartmentalised inflammation to structural and functional lung disease in CF: themysterious dichotomy of IL-6. Journal of Cystic Fibrosis. 23. S55–S55. 1 indexed citations

Fuchs, Thomas J., et al.. (2023). Machine learning approaches based on fibroblast morphometry do not predict ALS. Neurobiology of Aging. 130. 80–83. 1 indexed citations

Hirten, Robert, Matteo Danieletto, Micol Zweig, et al.. (2023). Development of the ehive Digital Health App: Protocol for a Centralized Research Platform. JMIR Research Protocols. 12. e49204–e49204. 5 indexed citations

Ho, David Joon, Narasimhan P. Agaram, Chad Vanderbilt, et al.. (2022). Deep Learning–Based Objective and Reproducible Osteosarcoma Chemotherapy Response Assessment and Outcome Prediction. American Journal Of Pathology. 193(3). 341–349. 15 indexed citations

D’Alfonso, Timothy M., David Joon Ho, Matthew G. Hanna, et al.. (2021). Multi-magnification-based machine learning as an ancillary tool for the pathologic assessment of shaved margins for breast carcinoma lumpectomy specimens. Modern Pathology. 34(8). 1487–1494. 16 indexed citations

Campanella, Gabriele, Cristián Navarrete‐Dechent, Konstantinos Liopyris, et al.. (2021). Deep Learning for Basal Cell Carcinoma Detection for Reflectance Confocal Microscopy. Journal of Investigative Dermatology. 142(1). 97–103. 37 indexed citations

10.

Vanderbilt, Chad, et al.. (2020). Beyond Classification: Whole Slide Tissue Histopathology Analysis By End-To-End Part Learning. 843–856. 9 indexed citations

11.

Puylaert, Carl A. J., Peter J. Schüffler, Jeroen A. W. Tielbeek, et al.. (2018). Semiautomatic Assessment of the Terminal Ileum and Colon in Patients with Crohn Disease Using MRI (the VIGOR++ Project). Academic Radiology. 25(8). 1038–1045. 23 indexed citations

12.

Campanella, Gabriele, et al.. (2017). Towards machine learned quality control: A benchmark for sharpness quantification in digital pathology. Computerized Medical Imaging and Graphics. 65. 142–151. 34 indexed citations

13.

Röth, Volker, et al.. (2014). Sparse meta-Gaussian information bottleneck. International Conference on Machine Learning. 910–918. 1 indexed citations

14.

Appel, Ron D., Thomas J. Fuchs, Piotr Dollár, & Pietro Perona. (2013). Quickly Boosting Decision Trees - Pruning Underachieving Features Early. CaltechAUTHORS (California Institute of Technology). 594–602. 73 indexed citations

15.

Thompson, David R., William Abbey, Abigail C. Allwood, et al.. (2012). Smart cameras for remote science survey. 7 indexed citations

16.

Wild, Peter J., Kristian Ikenberg, Thomas J. Fuchs, et al.. (2012). p53 suppresses type II endometrial carcinomas in mice and governs endometrial tumour aggressiveness in humans. EMBO Molecular Medicine. 4(8). 808–824. 52 indexed citations

17.

Vogt, Julia E., Sandhya Prabhakaran, Thomas J. Fuchs, & Volker Röth. (2010). The Translation-invariant Wishart-Dirichlet Process for Clustering Distance Data. International Conference on Machine Learning. 1111–1118. 8 indexed citations

18.

Raman, Sudhir, Thomas J. Fuchs, Peter J. Wild, et al.. (2010). Infinite mixture-of-experts model for sparse survival regression with application to breast cancer. BMC Bioinformatics. 11(S8). S8–S8. 10 indexed citations

19.

Fuchs, Thomas J., et al.. (2009). Graph-Based Pancreatic Islet Segmentation for Early Type 2 Diabetes Mellitus on Histopathological Tissue. Lecture notes in computer science. 12(Pt 2). 633–640. 4 indexed citations

20.

Gluz, Oleg, Peter J. Wild, Evelyn Ting, et al.. (2008). Nuclear karyopherin α2 expression predicts poor survival in patients with advanced breast cancer irrespective of treatment intensity. International Journal of Cancer. 123(6). 1433–1438. 66 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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