I am a research fellow with the School of Computer Science, and the Australia Research Council Training Centre for Innovative BioEngineering, the University of Sydney. Prior to that, I received my PhD degree (2018) from the School of Computer Science, the University of Sydney, supervised by Prof. David Dagan Feng, Prof. Jinman Kim and Clinical Professor Michael Fulham.
My current research interests include multi-modality medical image analysis, visualisation and radiomics. I also work closely with clinicians from major hospitals in China and Australia such as the Ruijin Hospital and Royal Prince Alfred Hospital, to translate and implement the developed medical technologies into clinical workflow.
E-mail: lei [DOT] bi [AT] sydney [DOT] edu [DOT] au
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| 32 |
Yuxin Xue, Lei Bi, Yige Peng, Michael Fulham, Dagan Feng, Jinman Kim, "PET Synthesis via Self-supervised Adaptive Residual Estimation Generative Adversarial Network," in IEEE Transactions on Radiation and Plasma Medical Sciences, 2023. IEEE PDF |
| 31 |
Lei Bi, Ulrich Buehner, Xiaohang Fu, Tom Williamson, Peter Choong, Jinman Kim, "Hybrid CNN-Transformer Network for Interactive Learning of Challenging Musculoskeletal Images," in Computer Methods and Programs in Biomedicine, 2023. Elsevier PDF |
| 30 |
B. Gu, M. Meng, M. Xu, D. Feng, L. Bi, J. Kim, S. Song, "Multi-task deep learning-based radiomic nomogram for prognostic prediction in locoregionally advanced nasopharyngeal carcinoma," in European Journal of Nuclear Medicine and Molecular Imaging , 2023. Springer PDF |
| 29 |
Lei Bi, M. Emre Celebi, Hitoshi Iyatomi, Pablo Fernandez-Penas, Jinman Kim, "Image Analysis in Advanced Skin Imaging Technology," in Computer Methods and Programs in Biomedicine, 2023. Elsevier PDF |
| 28 |
Wenxiang Ding, Qiaoqiao Ding, Kewei Chen, Miao Zhang, Li Lv, David Dagan Feng, Lei Bi, Jinman Kim, Qiu Huang, "A shortened model for Logan reference plot implemented via the self-supervised neural network for parametric PET imaging Submitting," in IEEE Transactions on Medical Imaging (TMI), 2023 (Accepted). IEEE Xplore PDF |
| 27 |
Lei Bi, Jinman Kim, Tingwei Su, Michael Fulham, David Dagan Feng, Guang Ning, "Deep Multi-Scale Resemblance Network for the Sub-class Differentiation of Adrenal Masses on Computed Tomography Images," in Artificial Intelligence in Medicine, 2022. Elsevier PDF |
| 26 |
Mingyuan Meng, Lei Bi, Michael Fulham, David Dagan Feng, Jinman Kim, "Enhancing Medical Image Registration via Appearance Adjustment Networks," in NeuroImage, 2022. Elsevier PDF |
| 25 |
Bingxin Gu, Mingyuan Meng, Lei Bi, Jinman Kim, David Dagan Feng, Shaoli Song, "Prediction of 5-year Progression-Free Survival in Advanced Nasopharyngeal Carcinoma with Pretreatment PET/CT using Multi-Modality Deep Learning-based Radiomics," in Frontiers in Oncology, 2022. Frontiers PDF |
| 24 |
Xiaohang Fu, Lei Bi, Ashnil Kumar, Michael Fulham, Jinman Kim, "Graph-Based Intercategory and Intermodality Network for Multilabel Classification and Melanoma Diagnosis of Skin Lesions in Dermoscopy and Clinical Images," in IEEE Transactions on Medical Imaging (TMI), 2022. IEEE Xplore PDF |
| 23 |
Mingyuan Meng, Bingxin Gu, Lei Bi, Shaoli Song, David Dagan Feng, Jinman Kim, "DeepMTS: Deep Multi-task Learning for Survival Prediction in Patients with Advanced Nasopharyngeal Carcinoma using Pretreatment PET/CT," in IEEE Journal of Biomedical and Health Informatics (JBHI), 2022. IEEE Xplore PDF |
| 22 |
Xiaoya Qiao, Chunjuan Jiang, Panli Li, Yuan Yuan, Qinglong Zeng, Lei Bi, Shaoli Song, Jinman Kim, Dagan Feng, Qiu Huang, "Improving Breast Tumor Segmentation in PET via Attentive Transformation Based Normalization," in IEEE Journal of Biomedical and Health Informatics (JBHI), 2022. IEEE Xplore |
| 21 |
Xiaohang Fu, Lei Bi, Ashnil Kumar, Michael Fulham, and Jinman Kim, "An Attention-Enhanced Cross-Task Network to Analyse Lung Nodule Attributes in CT Images," in Pattern Recognition (PR), 2022 (accepted). Elsevier PDF |
| 20 |
L. Bi, M. Fulham, J. Kim, "Hyper-Fusion Network for Semi-Automatic Segmentation of Skin Lesions," in Medical Image Analysis, 2021 (accepted). Elsevier PDF |
| 19 |
Yige Peng, Lei Bi, Ashnil Kumar, Michael Fulham, Dagan Feng, Jinman Kim, "Predicting distant metastases in soft-tissue sarcomas from PET-CT scans using constrained hierarchical multi-modality feature learning," in Physics in Medicine and Biology, 2021 (accepted). IOPscience PDF |
| 18 |
Yuyu Guo, Lei Bi, Dongming Wei, Liyun Chen, Zhengbin Zhu, Dagan Feng, Ruiyan Zhang, Qian Wang, Jinman Kim, "Unsupervised Landmark Detection Based Spatiotemporal Motion Estimation for 4D Dynamic Medical Images," in IEEE Transactions on Cybernetics, 2021 (accepted). IEEE Xplore PDF |
| 17 |
Joyce Zhanzi Wang, Jonathon Lillia, Muhannad Farhan, Lei Bi, Jinman Kim, Joshua Burns, Tegan L. Cheng, "Digital mapping of a manual fabrication method for paediatric ankle–foot orthoses," in Scientific Reports, 2021 (accepted). Scientific Reports PDF |
| 16 |
Y. Guo, L. Bi, Z. Zhu, D. Feng, R. Zhang, Q. Wang, J. Kim, "Automatic Left Ventricular Cavity Segmentation via Deep Spatial Sequential Network in 4D Computed Tomography," in Computerized Medical Imaging and Graphics (CMIG), 2021 (accepted). Elsevier PDF |
| 15 |
X. Wu, L. Bi, M. Fulham, D. Feng, L. Zhou, J. Kim, "Unsupervised Brain Tumor Segmentation using a Symmetric-driven Adversarial Network," in Neurocomputing, 2021 (accepted). Elsevier PDF |
| 14 |
L. Bi, M. Fulham, N. Li, Q. Liu, S. Song, D. Feng, J. Kim, "Recurrent Feature Fusion Learning for Multi-Modality PET-CT Tumor Segmentation," in Computer Methods and Programs in Biomedicine, 2021 (accepted). Elsevier PDF |
| 13 |
X. Fu, L. Bi, A. Kumar, M. Fulham, J. Kim, "Multimodal Spatial Attention Module for Targeting Multimodal PET-CT Lung Tumor Segmentation," in IEEE Journal of Biomedical and Health Informatics (JBHI), 2021 (accepted). IEEE Xplore PDF |
| 12 |
M. Meng, X. Yang, L. Bi, J. Kim, S. Xiao, Z. Yu, "High-parallelism Inception-like Spiking Neural Networks for Unsupervised Feature Learning," in Neurocomputing, 2021 (accepted). Elsevier PDF |
| 11 |
L. Bi, D. Feng, M. Fulham, J. Kim, "Multi-Label Classification of Multi-Modality Skin Lesion via Hyper-Connected Convolutional Neural Network," in Pattern Recognition (PR), vol 107, 2020. Elsevier PDF |
| 10 |
R. Huang, A. Nedanoski, D. F. Fletcher, N. Singh, J. Schmid, P. M. Young, N. Stow, L. Bi, D. Traini, E. Wong, C. L. Phillips, R. R. Grunstein, J. Kim, "An automated segmentation framework for nasal computational fluid dynamics analysis in computed tomography," in Computers in Biology and Medicine, 2019. Elsevier |
| 9 |
X. Liu, L. Bi, Y. Xu, D. Feng, J. Kim, X. Xu, "A Robust Deep Learning Method for Choroidal Vessels Segmentation on Swept Source Optical Coherence Tomography Images," in Biomedical Optics Express, , Volume 10, Issue 4, pp 1601-1612, 2019. OSA Open Access |
| 8 |
Y. Jung, J. Kim, L. Bi, A. Kumar, D. Feng, M. Fulham, "A direct volume rendering visualization approach for serial PET–CT scans that preserves anatomical consistency," in International Journal of Computer Assisted Radiology and Surgery (CARS), 2019. SpringerLink |
| 7 |
L. Bi, J. Kim, E. Ahn, A. Kumar, D. Feng and M. Fulham, "Step-wise Integration of Deep Class-specific Learning for Dermoscopic Image Segmentation," in Pattern Recognition (PR), Volume 85, pp 78-89, 2019. Elsevier PDF |
| 6 |
L. Bi, D. Feng and J. Kim, "Dual-Path Adversarial Learning for Fully Convolutional Network (FCN)-Based Medical Image Segmentation," in The Visual Computer Journal (TVCJ), Volume 34, Issue 6–8, pp 1043 – 1052, 2018. Springer PDF |
| 5 |
L. Bi, J. Kim, E. Ahn, A. Kumar, M. Fulham and D. Feng, "Dermoscopic Image Segmentation via Multi-Stage Fully Convolutional Networks," in IEEE Transactions on Biomedical Engineering (TBME), Volume 64, Issue 9, pp 2065 - 2074, 2017. IEEE Xplore PDF |
| 4 |
L. Bi, J. Kim, A. Kumar, M. Fulham and D. Feng, "Stacked Fully Convolutional Networks with Multi-Channel Learning: Application to Medical Image Segmentation," in The Visual Computer Journal (TVCJ), Volume 33, Issue 6-8, pp 1061-1071, 2017. Springer PDF |
| 3 |
E. Ahn, J. Kim, L. Bi, A. Kumar, C. Li, M. Fulham and D. Feng, "Saliency -based Lesion Segmentation via Background Detection in Dermoscopic Images," in IEEE Journal of Biomedical and Health Informatics (JBHI), Volume 21, Issue 6, pp 1685 - 1693, 2017. IEEE Xplore PDF |
| 2 |
L. Bi, J. Kim, A. Kumar, L. Wen, D. Feng and M. Fulham, "Automatic Detection and Classification of Regions of FDG Uptake in Whole-Body PET-CT Lymphoma Studies," in Computerized Medical Imaging and Graphics (CMIG), Volume 60, pp 3-10, 2017. Elsevier PDF |
| 1 |
A. Kumar, J. Kim, L. Bi, M. Fulham, and D. Feng, "Designing user interfaces to enhance human interpretation of medical content-based image retrieval: application to PET-CT images," in International Journal of Computer Assisted Radiology and Surgery (CARS), Volume 8, Issue 6, pp 1003-1014, 2013. SpringerLink |
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| 17 |
Mingyuan Meng, Dagan Feng, Lei Bi, Jinman Kim, "Correlation-aware Coarse-to-fine MLPs for Deformable Medical Image Registration," in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (Accepted) 2024. |
| 16 |
Mingyuan Meng, Lei Bi, Michael Fulham, Dagan Feng and Jinman Kim, "Merging-Diverging Hybrid Transformer Networks for Survival Prediction in Head and Neck Cancer," in International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), (Early Accepted) 2023. SpringerLink |
| 15 |
Mingyuan Meng, Lei Bi, Michael Fulham, Dagan Feng and Jinman Kim, "Non-iterative Coarse-to-fine Transformer Networks for Joint Affine and Deformable Image Registration," in International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), (Accepted) 2023. SpringerLink |
| 14 |
Mingyuan Meng, Lei Bi, Dagan Feng and Jinman Kim, "Non-iterative Coarse-to-fine Registration based on Single-pass Deep Cumulative Learning," in International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), (Accepted) 2022. SpringerLink |
| 13 |
Y. Peng, L. Bi, M. Fulham, D. Feng and J. Kim, "Multi-Modality Information Fusion for Radiomics-based Neural Architecture Search," in International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), (Early Accepted) 2020. SpringerLink |
| 12 |
X. Li, L. Bi, J. Kim, T. Li, B. Sheng and D. Feng, "Malocclusion Treatment Planning via PointNet based Spatial Transformation Network," in International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), (Early Accepted) 2020. SpringerLink |
| 11 |
Y. Guo, L. Bi, E. Ahn, D. Feng, Q. Wang, and J. Kim, "A Spatiotemporal Volumetric Interpolation Network for 4D Dynamic Medical Image," in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (accepted), 2020. PDF |
| 10 |
Y. Guo, L. Bi, A. Kumar, Y. Gao, R. Zhang, J. Kim, Q. Wang and D. Feng, "Deep Local-Global Refinement Network for Stent Analysis in IVOCT Images," in International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), pp539-546, (Early Accepted) 2019. SpringerLink PDF |
| 9 |
L. Bi, D. Feng, M. Fulham, and J. Kim, "Improving Skin Lesion Segmentation via Stacked Adversarial Learning," in International Symposium on Biomedical Imaging: From Nano to Macro (ISBI), 2019. IEEE Xplore PDF |
| 8 |
L. Bi, J. Kim, T. Su, M. Fulham, D. Feng, and G. Ning, "Adrenal Lesions Detection on Low-Contrast CT Images using Fully Convolutional Networks with Multi-Scale Integration," in International Symposium on Biomedical Imaging: From Nano to Macro (ISBI), pp895-898, 2017. IEEE Xplore PDF |
| 7 |
L. Bi, J. Kim, E. Ahn, D. Feng, and M. Fulham, "Semi-Automatic Skin Lesion Segmentation via Fully Convolutional Networks," in International Symposium on Biomedical Imaging: From Nano to Macro (ISBI), pp561-564, 2017. IEEE Xplore PDF |
| 6 |
L. Bi, J. Kim, E. Ahn, D. Feng, and M. Fulham, "Automated Skin Lesion Segmentation via Image-wise Supervised Learning and Multi-scale Superpixel Based Cellular Automata," in International Symposium on Biomedical Imaging: From Nano to Macro (ISBI), pp1059-1062, 2016. IEEE Xplore PDF Code Available |
| 5 |
L. Bi, J. Kim, E. Ahn, D. Feng, and M. Fulham, "Automatic Melanoma Detection via Multi-scale Lesion-biased Representation and Joint Reverse Classification," in International Symposium on Biomedical Imaging: From Nano to Macro (ISBI), pp1055-1058, 2016. IEEE Xplore PDF |
| 4 |
R. Huang, A. Li, L. Bi, C. Li, P. Young, J. Kim and D. Feng, "A Locally Constrained Statistical Shape Model for Robust Nasal Cavity Segmentation in Computed Tomography," in International Symposium on Biomedical Imaging: From Nano to Macro (ISBI), pp1334-1337, 2016. IEEE Xplore PDF |
| 3 |
L. Bi, J. Kim, L. Wen, D. Feng, and M. Fulham, "Automated Thresholded Region Classification Using A Robust Feature Selection Method For PET-CT," in International Symposium on Biomedical Imaging: From Nano to Macro (ISBI), pp1435-1438, 2015. IEEE Xplore PDF |
| 2 |
L. Bi, J. Kim, A. Kumar, D. Feng, and M. Fulham, "Adaptive Supervoxel Patch-based Region Classification in Whole-Body PET-CT," in Medical Image Computing and Computer Assisted Intervention (MICCAI) - Computational Methods for Molecular Imaging (CMMI), 2015. SpringerLink PDF Best Paper Award |
| 1 |
L. Bi, J. Kim, D. Feng, and M. Fulham, "Multi-stage Thresholded Region Classification for Whole-Body PET-CT Lymphoma Studies," in The 17th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), pp569-576, 2014. SpringerLink PDF |
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| 1 |
A. Kumar, L. Bi, J. Kim, D. Feng, "Machine learning in medical imaging," in Biomedical Information Technology, Academic Press, 167-196, 2020. Elsevier Book Chapter |