Jiatai Lin

715 total citations · 1 hit paper
18 papers, 417 citations indexed

About

Jiatai Lin is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging and Computer Vision and Pattern Recognition. According to data from OpenAlex, Jiatai Lin has authored 18 papers receiving a total of 417 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Artificial Intelligence, 8 papers in Radiology, Nuclear Medicine and Imaging and 7 papers in Computer Vision and Pattern Recognition. Recurrent topics in Jiatai Lin's work include AI in cancer detection (10 papers), Radiomics and Machine Learning in Medical Imaging (6 papers) and Digital Imaging for Blood Diseases (4 papers). Jiatai Lin is often cited by papers focused on AI in cancer detection (10 papers), Radiomics and Machine Learning in Medical Imaging (6 papers) and Digital Imaging for Blood Diseases (4 papers). Jiatai Lin collaborates with scholars based in China, Hong Kong and United States. Jiatai Lin's co-authors include Chu Han, Zaiyi Liu, Xipeng Pan, Zhenwei Shi, Bingchao Zhao, Zhi Liu, Zeyan Xu, Yun Zhang, Guoqiang Han and Bingjiang Qiu and has published in prestigious journals such as IEEE Access, IEEE Transactions on Medical Imaging and IEEE Transactions on Cybernetics.

In The Last Decade

Jiatai Lin

17 papers receiving 405 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.

CKD-TransBTS: Clinical Knowledge-Driven Hybrid Transformer With Modality-Correlated Cross-Attention for Brain Tumor Segmentation

2023 103 citations Jianwei Lin, Jiatai Lin et al. IEEE Transactions on Medical Imaging profile →

Peers

Jiatai Lin

CVPR

RNMI

NEURO

Boqiang Liu China

K. Balasamy India

Yujiro Furukawa Japan

Jing Xue China

Tae‐Seong Kim South Korea

Ryosuke Araki Japan

A. K. M. Rakibul Haque Rafid Australia

Yin Dai China

Biswajit Jena India

Kai Han China

Boqiang Liu China

Jiatai Lin

183 ×0.8

201 ×1.1

CVPR

205 ×1.2

RNMI

124 ×1.1

NEURO

88 ×1.9

Citations per year, relative to Jiatai Lin Jiatai Lin (= 1×) peers Boqiang Liu

Countries citing papers authored by Jiatai Lin

Since Specialization

Citations

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

Fields of papers citing papers by Jiatai Lin

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jiatai Lin

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

All Works

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18 of 18 papers shown

Lin, Jiatai, Hao Wang, Danyi Li, et al.. (2025). Rethinking mitosis detection: Towards diverse data and feature representation for better domain generalization. Artificial Intelligence in Medicine. 163. 103097–103097.

Liu, Yu, Jiatai Lin, Zhenwei Shi, et al.. (2025). FedBCD: Federated Ultrasound Video and Image Joint Learning for Breast Cancer Diagnosis. IEEE Transactions on Medical Imaging. 44(6). 2395–2407. 1 indexed citations

Xu, Peng, Yuankui Wu, Chu Han, et al.. (2024). CroMAM: A Cross-Magnification Attention Feature Fusion Model for Predicting Genetic Status and Survival of Gliomas Using Histological Images. IEEE Journal of Biomedical and Health Informatics. 28(12). 7345–7356. 3 indexed citations

Xia, Yingda, Zhihong Chen, Suyun Li, et al.. (2024). A Colorectal Coordinate-Driven Method for Colorectum and Colorectal Cancer Segmentation in Conventional CT Scans. IEEE Transactions on Neural Networks and Learning Systems. 36(4). 7395–7406. 4 indexed citations

Huang, Yanqi, Guoqiang Han, Zhenwei Shi, et al.. (2024). FedDBL: Communication and Data Efficient Federated Deep-Broad Learning for Histopathological Tissue Classification. IEEE Transactions on Cybernetics. 54(12). 7851–7864. 6 indexed citations

Lin, Jianwei, Jiatai Lin, Hao Chen, et al.. (2023). CKD-TransBTS: Clinical Knowledge-Driven Hybrid Transformer With Modality-Correlated Cross-Attention for Brain Tumor Segmentation. IEEE Transactions on Medical Imaging. 42(8). 2451–2461. 103 indexed citations breakdown →

Han, Chu, Yü Liu, Zhenwei Shi, et al.. (2023). HoVer-Trans: Anatomy-Aware HoVer-Transformer for ROI-Free Breast Cancer Diagnosis in Ultrasound Images. IEEE Transactions on Medical Imaging. 42(6). 1696–1706. 72 indexed citations

Lin, Jiatai, Guoqiang Han, Xipeng Pan, et al.. (2022). PDBL: Improving Histopathological Tissue Classification With Plug-and-Play Pyramidal Deep-Broad Learning. IEEE Transactions on Medical Imaging. 41(9). 2252–2262. 39 indexed citations

Han, Chu, Jiatai Lin, Yi Wang, et al.. (2022). Multi-layer pseudo-supervision for histopathology tissue semantic segmentation using patch-level classification labels. Medical Image Analysis. 80. 102487–102487. 68 indexed citations

10.

Pan, Xipeng, Huan Lin, Chu Han, et al.. (2022). Computerized tumor-infiltrating lymphocytes density score predicts survival of patients with resectable lung adenocarcinoma. iScience. 25(12). 105605–105605. 30 indexed citations

11.

Zhao, Bingchao, Chu Han, Xipeng Pan, et al.. (2022). RestainNet: A self-supervised digital re-stainer for stain normalization. Computers & Electrical Engineering. 103. 108304–108304. 18 indexed citations

12.

Lin, Jiatai, et al.. (2020). Towards Accurate Pulmonary Nodule Detection by Representing Nodules as Points With High-Resolution Network. IEEE Access. 8. 157391–157402. 7 indexed citations

13.

Lin, Jiatai, Zhi Liu, C. L. Philip Chen, & Yun Zhang. (2019). A wavelet broad learning adaptive filter for forecasting and cancelling the physiological tremor in teleoperation. Neurocomputing. 356. 170–183. 19 indexed citations

14.

Lin, Jiatai, et al.. (2019). Quaternion broad learning system: A novel multi-dimensional filter for estimation and elimination tremor in teleoperation. Neurocomputing. 380. 78–86. 20 indexed citations

15.

Lin, Jiatai, Zhi Liu, C. L. Philip Chen, & Yun Zhang. (2019). Three-domain fuzzy wavelet broad learning system for tremor estimation. Knowledge-Based Systems. 192. 105295–105295. 18 indexed citations

16.

Liu, Zhi, et al.. (2019). Loop Closure Detection for Visual SLAM Systems Using Various CNN algorithms Contrasts. 1663–1668. 3 indexed citations

17.

Lin, Jiatai, et al.. (2019). sEMG Gestures Recognition Based on Wavelet Broad Learning System. 54. 1658–1662. 5 indexed citations

18.

Lin, Jiatai, et al.. (2018). Wavelet Broad Learning Filter: A Novel Adaptive Filter for estimating the Physiological Tremor in Teleoperation. 354–359. 1 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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