Michael Fulham

14.3k total citations · 4 hit papers
269 papers, 9.0k citations indexed

About

Michael Fulham is a scholar working on Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Michael Fulham has authored 269 papers receiving a total of 9.0k indexed citations (citations by other indexed papers that have themselves been cited), including 123 papers in Radiology, Nuclear Medicine and Imaging, 83 papers in Computer Vision and Pattern Recognition and 44 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Michael Fulham's work include Medical Imaging Techniques and Applications (86 papers), Radiomics and Machine Learning in Medical Imaging (66 papers) and Medical Image Segmentation Techniques (53 papers). Michael Fulham is often cited by papers focused on Medical Imaging Techniques and Applications (86 papers), Radiomics and Machine Learning in Medical Imaging (66 papers) and Medical Image Segmentation Techniques (53 papers). Michael Fulham collaborates with scholars based in Australia, China and United States. Michael Fulham's co-authors include Dagan Feng, Jinman Kim, Ashnil Kumar, Stefan Eberl, Weidong Cai, Lei Bi, Yong Xia, Euijoon Ahn, Steven R. Meikle and Jianpeng Zhang and has published in prestigious journals such as Journal of Clinical Oncology, SHILAP Revista de lepidopterología and Blood.

In The Last Decade

Michael Fulham

263 papers receiving 8.8k citations

Hit Papers

align trajectories sort by overperformance

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.

Safety and activity of microRNA-loaded minicells in patients with recurrent malignant pleural mesothelioma: a first-in-man, phase 1, open-label, dose-escalation study

2017 539 citations Michael Fulham et al. profile →
Multimodal Neuroimaging Feature Learning for Multiclass Diagnosis of Alzheimer's Disease

2014 404 citations Weidong Cai, Dagan Feng et al. profile →
An Ensemble of Fine-Tuned Convolutional Neural Networks for Medical Image Classification

2016 382 citations Ashnil Kumar, Jinman Kim et al. IEEE Journal of Biomedical and Health Informatics profile →
Knowledge-based Collaborative Deep Learning for Benign-Malignant Lung Nodule Classification on Chest CT

2018 351 citations Yutong Xie, Yong Xia et al. IEEE Transactions on Medical Imaging profile →

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author						Papers	Cites
Michael Fulham	3.7k	1.8k	1.7k	1.5k	1.4k	269	9.0k
Bradley J. Erickson	4.5k 1.2×	1.8k 1.0×	1.4k 0.8×	1.5k 1.0×	506 0.4×	280	10.4k
Bin Zheng	3.9k 1.1×	3.6k 2.1×	1.5k 0.9×	2.1k 1.4×	818 0.6×	392	7.8k
Yuanyuan Wang	3.3k 0.9×	1.2k 0.7×	1.1k 0.6×	914 0.6×	508 0.4×	533	8.2k
David Snead	2.3k 0.6×	2.4k 1.4×	1.2k 0.7×	663 0.5×	980 0.7×	128	6.6k
Ayman El‐Baz	4.3k 1.2×	1.9k 1.1×	1.8k 1.1×	1.6k 1.1×	352 0.3×	665	11.4k
Namkug Kim	4.6k 1.2×	1.2k 0.7×	722 0.4×	2.9k 2.0×	718 0.5×	412	10.9k
Guang Yang	4.7k 1.3×	1.7k 1.0×	2.5k 1.5×	1.2k 0.8×	264 0.2×	449	11.3k
Dong Ni	2.2k 0.6×	2.5k 1.4×	1.8k 1.1×	653 0.4×	701 0.5×	232	6.8k
Takeshi Hara	2.2k 0.6×	901 0.5×	1.1k 0.7×	733 0.5×	598 0.4×	327	5.5k
Zhenyu Liu	5.3k 1.4×	1.3k 0.8×	438 0.3×	1.9k 1.3×	1.4k 1.1×	361	9.9k

Countries citing papers authored by Michael Fulham

Since Specialization

Citations

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

Fields of papers citing papers by Michael Fulham

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Michael Fulham

This figure shows the co-authorship network connecting the top 25 collaborators of Michael Fulham. A scholar is included among the top collaborators of Michael Fulham 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 Michael Fulham. Michael Fulham 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

Meng, Mingyuan, et al.. (2025). Enhancing Medical Vision-Language Contrastive Learning via Inter-Matching Relation Modeling. IEEE Transactions on Medical Imaging. 44(6). 2463–2476. 2 indexed citations

Jung, Younhyun, et al.. (2024). A Generative Adversarial Network for Upsampling of Direct Volume Rendering Images. Computer Graphics Forum. 44(1).

Bi, Lei, Xiaohang Fu, Qiufang Liu, et al.. (2024). Co-Learning Multimodality PET-CT Features via a Cascaded CNN-Transformer Network. IEEE Transactions on Radiation and Plasma Medical Sciences. 8(7). 814–825.

Bi, Lei, et al.. (2023). PET Synthesis via Self-Supervised Adaptive Residual Estimation Generative Adversarial Network. IEEE Transactions on Radiation and Plasma Medical Sciences. 8(4). 426–438. 2 indexed citations

Ono, Maiko, Tomoteru Yamasaki, Katsushi Kumata, et al.. (2023). Synthesis and structure–activity relationship (SAR) studies of 1,2,3-triazole, amide, and ester-based benzothiazole derivatives as potential molecular probes for tau protein. RSC Medicinal Chemistry. 14(5). 858–868. 6 indexed citations

Xia, Tian, Ashnil Kumar, Michael Fulham, et al.. (2022). Fused feature signatures to probe tumour radiogenomics relationships. Scientific Reports. 12(1). 2173–2173. 4 indexed citations

Bi, Lei, et al.. (2021). Predicting Distant Metastases in Soft-Tissue Sarcomas from PET-CT scans using Constrained Hierarchical Multi-Modality Feature Learning. arXiv (Cornell University). 3 indexed citations

Ahn, Euijoon, Ashnil Kumar, Michael Fulham, Dagan Feng, & Jinman Kim. (2020). Unsupervised Domain Adaptation to Classify Medical Images Using Zero-Bias Convolutional Auto-Encoders and Context-Based Feature Augmentation. IEEE Transactions on Medical Imaging. 39(7). 2385–2394. 38 indexed citations

Ahn, Euijoon, Ashnil Kumar, Dagan Feng, Michael Fulham, & Jinman Kim. (2019). Unsupervised Deep Transfer Feature Learning for Medical Image Classification. 1915–1918. 27 indexed citations

10.

Kumar, Ashnil, et al.. (2018). Unsupervised Two-Path Neural Network for Cell Event Detection and Classification Using Spatiotemporal Patterns. IEEE Transactions on Medical Imaging. 38(6). 1477–1487. 13 indexed citations

11.

Xie, Yutong, Yong Xia, Jianpeng Zhang, et al.. (2018). Knowledge-based Collaborative Deep Learning for Benign-Malignant Lung Nodule Classification on Chest CT. IEEE Transactions on Medical Imaging. 38(4). 991–1004. 351 indexed citations breakdown →

12.

Ahn, Euijoon, Jinman Kim, Lei Bi, et al.. (2017). Saliency-Based Lesion Segmentation Via Background Detection in Dermoscopic Images. IEEE Journal of Biomedical and Health Informatics. 21(6). 1685–1693. 110 indexed citations

13.

Zhang, Jianpeng, Yong Xia, Yutong Xie, Michael Fulham, & Dagan Feng. (2017). Classification of Medical Images in the Biomedical Literature by Jointly Using Deep and Handcrafted Visual Features. IEEE Journal of Biomedical and Health Informatics. 22(5). 1521–1530. 88 indexed citations

14.

Xie, Yutong, Jianpeng Zhang, Yong Xia, Michael Fulham, & Yanning Zhang. (2017). Fusing texture, shape and deep model-learned information at decision level for automated classification of lung nodules on chest CT. Information Fusion. 42. 102–110. 212 indexed citations

15.

Tareef, Afaf, Yang Song, Weidong Cai, et al.. (2016). Automatic segmentation of overlapping cervical smear cells based on local distinctive features and guided shape deformation. Neurocomputing. 221. 94–107. 52 indexed citations

16.

Jung, Younhyun, Jinman Kim, Ashnil Kumar, Dagan Feng, & Michael Fulham. (2016). Efficient visibility-driven medical image visualisation via adaptive binned visibility histogram. Computerized Medical Imaging and Graphics. 51. 40–49. 7 indexed citations

17.

Shen, Lu, et al.. (2014). Automated Identification of Dementia Using FDG-PET Imaging. BioMed Research International. 2014. 1–8. 18 indexed citations

18.

Li, Ang, Changyang Li, Xiuying Wang, et al.. (2013). Automated Segmentation of Prostate MR Images Using Prior Knowledge Enhanced Random Walker. 1–7. 12 indexed citations

19.

Barrington, Sally F., Michael O’Doherty, Lucy Pike, et al.. (2011). Standardised PET-CT reporting for an international multicentre trial in lymphoma (RATHL). European Journal of Nuclear Medicine and Molecular Imaging. 38. 2 indexed citations

20.

Kassiou, Michael, Stefan Eberl, Steven R. Meikle, et al.. (2001). In vivo imaging of nicotinic receptor upregulation following chronic (-)-nicotine treatment in baboon using SPECT. Nuclear Medicine and Biology. 28(2). 165–175. 41 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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