Maxine Tan

1.8k total citations
44 papers, 1.1k citations indexed

About

Maxine Tan is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Maxine Tan has authored 44 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 31 papers in Artificial Intelligence, 25 papers in Radiology, Nuclear Medicine and Imaging and 23 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Maxine Tan's work include AI in cancer detection (29 papers), Radiomics and Machine Learning in Medical Imaging (24 papers) and Digital Radiography and Breast Imaging (14 papers). Maxine Tan is often cited by papers focused on AI in cancer detection (29 papers), Radiomics and Machine Learning in Medical Imaging (24 papers) and Digital Radiography and Breast Imaging (14 papers). Maxine Tan collaborates with scholars based in United States, Malaysia and Belgium. Maxine Tan's co-authors include Bin Zheng, Hong Liu, Jiantao Pu, Rudi Deklerck, Bart Jansen, Jan Cornelis, Michel Bister, Yuchen Qiu, David Gur and Samuel Cheng and has published in prestigious journals such as IEEE Access, IEEE Transactions on Biomedical Engineering and IEEE Transactions on Medical Imaging.

In The Last Decade

Maxine Tan

43 papers receiving 1.1k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Maxine Tan United States 20 830 622 555 144 141 44 1.1k
Yali Zang China 20 1.6k 1.9× 411 0.7× 926 1.7× 203 1.4× 244 1.7× 42 2.1k
Alessandro Stefano Italy 25 880 1.1× 207 0.3× 431 0.8× 124 0.9× 183 1.3× 82 1.3k
Jonas Teuwen Netherlands 15 962 1.2× 671 1.1× 268 0.5× 120 0.8× 120 0.9× 62 1.4k
Si‐Wa Chan Taiwan 15 530 0.6× 369 0.6× 296 0.5× 96 0.7× 80 0.6× 44 879
Fahdi Kanavati Japan 11 504 0.6× 500 0.8× 168 0.3× 218 1.5× 122 0.9× 20 794
Guy Nir Canada 14 501 0.6× 438 0.7× 307 0.6× 73 0.5× 177 1.3× 26 966
Annarita Fanizzi Italy 23 720 0.9× 594 1.0× 217 0.4× 167 1.2× 66 0.5× 88 1.2k
Jiangdian Song China 18 1.2k 1.4× 285 0.5× 696 1.3× 163 1.1× 43 0.3× 40 1.3k
Yiwen Xu United States 9 467 0.6× 246 0.4× 392 0.7× 130 0.9× 45 0.3× 22 803
Lena Costaridou Greece 18 850 1.0× 715 1.1× 380 0.7× 56 0.4× 506 3.6× 86 1.5k

Countries citing papers authored by Maxine Tan

Since Specialization
Citations

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

Fields of papers citing papers by Maxine Tan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Maxine Tan

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

All Works

20 of 20 papers shown
1.
Nguyen, Tuong L., Gillian S. Dite, Robert J. MacInnis, et al.. (2023). Causal relationships between breast cancer risk factors based on mammographic features. Breast Cancer Research. 25(1). 127–127. 2 indexed citations
2.
Tan, Maxine, et al.. (2021). 3D axial-attention for lung nodule classification. International Journal of Computer Assisted Radiology and Surgery. 16(8). 1319–1324. 22 indexed citations
3.
Lan, Boon Leong, et al.. (2021). CASPIANET++: A multidimensional Channel-Spatial Asymmetric attention network with Noisy Student Curriculum Learning paradigm for brain tumor segmentation. Computers in Biology and Medicine. 136. 104690–104690. 12 indexed citations
4.
Tan, Maxine, et al.. (2021). ProCAN: Progressive growing channel attentive non-local network for lung nodule classification. Pattern Recognition. 122. 108309–108309. 52 indexed citations
5.
Lan, Boon Leong, et al.. (2019). Lung nodule classification using deep Local–Global networks. International Journal of Computer Assisted Radiology and Surgery. 14(10). 1815–1819. 62 indexed citations
6.
Lee, Hwee Kuan, et al.. (2019). Gated-Dilated Networks for Lung Nodule Classification in CT Scans. IEEE Access. 7. 178827–178838. 46 indexed citations
7.
Tan, Maxine, Shivaani Mariapun, Cheng Har Yip, Kwan Hoong Ng, & Soo‐Hwang Teo. (2018). A novel method of determining breast cancer risk using parenchymal textural analysis of mammography images on an Asian cohort. Physics in Medicine and Biology. 64(3). 35016–35016. 14 indexed citations
8.
Tan, Maxine, et al.. (2016). Developing a new case based computer-aided detection scheme and an adaptive cueing method to improve performance in detecting mammographic lesions. Physics in Medicine and Biology. 62(2). 358–376. 12 indexed citations
9.
Qiu, Yuchen, Shiju Yan, Maxine Tan, et al.. (2016). Computer-aided classification of mammographic masses using the deep learning technology: a preliminary study. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 9785. 978520–978520. 23 indexed citations
10.
Tan, Maxine, Bin Zheng, Joseph K. Leader, & David Gur. (2016). Association Between Changes in Mammographic Image Features and Risk for Near-Term Breast Cancer Development. IEEE Transactions on Medical Imaging. 35(7). 1719–1728. 57 indexed citations
11.
12.
Tan, Maxine, Jiantao Pu, Samuel Cheng, Hong Liu, & Bin Zheng. (2015). Assessment of a Four-View Mammographic Image Feature Based Fusion Model to Predict Near-Term Breast Cancer Risk. Annals of Biomedical Engineering. 43(10). 2416–2428. 55 indexed citations
13.
Tan, Maxine, Wei Qian, Jiantao Pu, Hong Liu, & Bin Zheng. (2015). A new approach to develop computer-aided detection schemes of digital mammograms. Physics in Medicine and Biology. 60(11). 4413–4427. 33 indexed citations
14.
Tan, Maxine, et al.. (2015). Computer‐aided breast MR image feature analysis for prediction of tumor response to chemotherapy. Medical Physics. 42(11). 6520–6528. 46 indexed citations
15.
Tan, Maxine, Jiantao Pu, & Bin Zheng. (2014). Reduction of false-positive recalls using a computerized mammographic image feature analysis scheme. Physics in Medicine and Biology. 59(15). 4357–4373. 27 indexed citations
16.
Tan, Maxine, Jiantao Pu, & Bin Zheng. (2014). A new and fast image feature selection method for developing an optimal mammographic mass detection scheme. Medical Physics. 41(8Part1). 81906–81906. 9 indexed citations
17.
Tan, Maxine, Jiantao Pu, & Bin Zheng. (2014). Optimization of breast mass classification using sequential forward floating selection (SFFS) and a support vector machine (SVM) model. International Journal of Computer Assisted Radiology and Surgery. 9(6). 1005–1020. 64 indexed citations
18.
Tan, Maxine, Rudi Deklerck, Jan Cornelis, & Bart Jansen. (2013). Phased searching with NEAT in a Time-Scaled Framework: Experiments on a computer-aided detection system for lung nodules. Artificial Intelligence in Medicine. 59(3). 157–167. 24 indexed citations
19.
Tan, Maxine, et al.. (2013). Prediction of Near-term Breast Cancer Risk Based on Bilateral Mammographic Feature Asymmetry. Academic Radiology. 20(12). 1542–1550. 52 indexed citations
20.
Tan, Maxine, Rudi Deklerck, Bart Jansen, & Jan Cornelis. (2012). Analysis of a feature-deselective neuroevolution classifier (FD-NEAT) in a computer-aided lung nodule detection system for CT images. 539–546. 5 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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