Luyang Luo

1.2k total citations · 1 hit paper
26 papers, 706 citations indexed

About

Luyang Luo is a scholar working on Radiology, Nuclear Medicine and Imaging, Artificial Intelligence and Computer Vision and Pattern Recognition. According to data from OpenAlex, Luyang Luo has authored 26 papers receiving a total of 706 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Radiology, Nuclear Medicine and Imaging, 10 papers in Artificial Intelligence and 8 papers in Computer Vision and Pattern Recognition. Recurrent topics in Luyang Luo's work include Radiomics and Machine Learning in Medical Imaging (11 papers), AI in cancer detection (7 papers) and Medical Image Segmentation Techniques (4 papers). Luyang Luo is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (11 papers), AI in cancer detection (7 papers) and Medical Image Segmentation Techniques (4 papers). Luyang Luo collaborates with scholars based in Hong Kong, China and United States. Luyang Luo's co-authors include Hao Chen, Pheng‐Ann Heng, Xi Wang, Qi Dou, Bei Yu, Haoyu Yang, Jing Su, Chenxi Lin, An Ran Ran and Carol Y. Cheung and has published in prestigious journals such as Nature Communications, IEEE Transactions on Medical Imaging and Investigative Ophthalmology & Visual Science.

In The Last Decade

Luyang Luo

22 papers receiving 688 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.

Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions

2024 70 citations Luyang Luo, Xi Wang et al. IEEE Reviews in Biomedical Engineering profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Luyang Luo Hong Kong	11	431	308	146	135	108	26	706
Antônio Oseas de Carvalho Filho Brazil	17	621 1.4×	343 1.1×	102 0.7×	199 1.5×	25 0.2×	45	816
Lituan Wang China	10	197 0.5×	247 0.8×	37 0.3×	209 1.5×	16 0.1×	23	487
Ivan Cruz‐Aceves Mexico	15	303 0.7×	113 0.4×	75 0.5×	314 2.3×	12 0.1×	52	656
Sertan Serte Cyprus	15	733 1.7×	447 1.5×	139 1.0×	263 1.9×	9 0.1×	36	1.0k
Sarmad Maqsood Lithuania	11	225 0.5×	293 1.0×	36 0.2×	310 2.3×	21 0.2×	20	701
Anum Masood Pakistan	14	342 0.8×	317 1.0×	11 0.1×	118 0.9×	41 0.4×	54	752
Rongchang Zhao China	15	554 1.3×	128 0.4×	366 2.5×	367 2.7×	14 0.1×	41	767
Hiam Alquran Jordan	15	257 0.6×	358 1.2×	31 0.2×	105 0.8×	8 0.1×	71	816
Mohammad H. Jafari Canada	10	191 0.4×	302 1.0×	12 0.1×	151 1.1×	21 0.2×	26	663

Countries citing papers authored by Luyang Luo

Since Specialization

Citations

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

Fields of papers citing papers by Luyang Luo

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Luyang Luo

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

20 of 20 papers shown

Luo, Luyang, et al.. (2025). SurgPETL: Parameter-Efficient Image-to-Surgical-Video Transfer Learning for Surgical Phase Recognition. IEEE Transactions on Medical Imaging. 45(3). 1013–1023.

Luo, Luyang, et al.. (2025). Mitigating medical dataset bias by learning adaptive agreement from a biased council. Medical Image Analysis. 105. 103629–103629.

Wu, Linshan, Yanning Zhou, Luyang Luo, et al.. (2025). Large-scale generative tumor synthesis in computed tomography images for improving tumor recognition. Nature Communications. 16(1). 11053–11053.

Luo, Luyang, et al.. (2025). Learning robust medical image segmentation from multi-source annotations. Medical Image Analysis. 101. 103489–103489. 2 indexed citations

Luo, Luyang, Xin Yi, Qiong Wang, et al.. (2025). A large model for non-invasive and personalized management of breast cancer from multiparametric MRI. Nature Communications. 16(1). 3647–3647.

Luo, Luyang, et al.. (2024). Scale-Aware Super-Resolution Network With Dual Affinity Learning for Lesion Segmentation From Medical Images. IEEE Transactions on Neural Networks and Learning Systems. 36(6). 10530–10543. 2 indexed citations

Luo, Luyang, Xi Wang, Yi Lin, et al.. (2024). Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions. IEEE Reviews in Biomedical Engineering. 18. 130–151. 70 indexed citations breakdown →

Luo, Luyang, et al.. (2024). MICA: Towards Explainable Skin Lesion Diagnosis via Multi-Level Image-Concept Alignment. Proceedings of the AAAI Conference on Artificial Intelligence. 38(2). 837–845. 5 indexed citations

Luo, Luyang, Ruofeng Tong, Yen‐Wei Chen, et al.. (2024). Rethinking Multiple Instance Learning for Whole Slide Image Classification: A Bag-Level Classifier is a Good Instance-Level Teacher. IEEE Transactions on Medical Imaging. 43(11). 3964–3976. 5 indexed citations

10.

Jin, Cheng, et al.. (2024). HMIL: Hierarchical Multi-Instance Learning for Fine-Grained Whole Slide Image Classification. IEEE Transactions on Medical Imaging. 44(4). 1796–1808. 3 indexed citations

11.

Li, Fang, Chunyan Li, Lixian Liu, et al.. (2024). Development and validation of an interpretable model integrating multimodal information for improving ovarian cancer diagnosis. Nature Communications. 15(1). 2681–2681. 24 indexed citations

12.

Chen, Hao & Luyang Luo. (2023). Trustworthy Machine Learning for Healthcare. Lecture notes in computer science. 4 indexed citations

13.

Zhang, Yuhan, Luyang Luo, Qi Dou, & Pheng‐Ann Heng. (2023). Triplet attention and dual-pool contrastive learning for clinic-driven multi-label medical image classification. Medical Image Analysis. 86. 102772–102772. 39 indexed citations

14.

Luo, Luyang, Hao Chen, Yanning Zhou, et al.. (2022). Rethinking Annotation Granularity for Overcoming Shortcuts in Deep Learning–based Radiograph Diagnosis: A Multicenter Study. Radiology Artificial Intelligence. 4(5). e210299–e210299. 18 indexed citations

15.

Luo, Luyang, et al.. (2022). Deep Semi-Supervised Metric Learning with Dual Alignment for Cervical Cancer Cell Detection. 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI). 8 indexed citations

16.

Wang, Xi, Hao Chen, An Ran Ran, et al.. (2020). Towards multi-center glaucoma OCT image screening with semi-supervised joint structure and function multi-task learning. Medical Image Analysis. 63. 101695–101695. 65 indexed citations

17.

Wang, Xi, Fangyao Tang, Hao Chen, et al.. (2020). UD-MIL: Uncertainty-Driven Deep Multiple Instance Learning for OCT Image Classification. IEEE Journal of Biomedical and Health Informatics. 24(12). 3431–3442. 56 indexed citations

18.

Zhou, Juan, Luyang Luo, Qi Dou, et al.. (2019). Weakly supervised 3D deep learning for breast cancer classification and localization of the lesions in MR images. Journal of Magnetic Resonance Imaging. 50(4). 1144–1151. 118 indexed citations

19.

Ran, An Ran, Xi Wang, Luyang Luo, et al.. (2019). A 3D Deep Learning System for Detecting Glaucomatous Optic Neuropathy from Volumetric and En Face Optical Coherence Tomography Scans. Investigative Ophthalmology & Visual Science. 60(9). 5571–5571. 1 indexed citations

20.

Ran, An Ran, Carol Y. Cheung, Xi Wang, et al.. (2019). Detection of glaucomatous optic neuropathy with spectral-domain optical coherence tomography: a retrospective training and validation deep-learning analysis. The Lancet Digital Health. 1(4). e172–e182. 103 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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