Publications

2024

1. CIBM

   DN-ODE: Data-driven neural-ODE modeling for breast cancer tumor dynamics and progression-free survivals

   Jinlin Xiang, Bozhao Qi, Marc Cerou, and 2 more authors

   Computers in Biology and Medicine 2024

   Abs

   Pharmacokinetic/Pharmacodynamic (PK/PD) modeling is crucial in the development of new drugs. However, traditional population-based PK/PD models encounter challenges when modeling for individual patients. We aim to explore the potential of constructing a pharmacodynamic model for individual breast cancer pharmacodynamics leveraging only limited data from early clinical trial phases. While previous studies on Neural Ordinary Differential Equations (ODEs) suggest promising results in clinical trial practices, they primarily focused on theoretical applications or independent PK/PD modeling. PD modeling from complex and irregular clinical trial data, especially when interacting with PK parameters, is still unclear. To achieve that, we introduce a Data-driven Neural Ordinary Differential Equation (DN-ODE) modeling for breast cancer tumor dynamics and progression-free survival data. To validate this approach, experiments are conducted with early-phase clinical trial data from the Amcenestrant (an oral treatment for breast cancer) dataset (AMEERA 1-2), aiming to predict pharmacodynamics in the later phase (AMEERA 3). DN-ODE model achieves RMSE scores of 8.78 and 0.21 in tumor size and progression-free survival, respectively, with R2 scores over 0.9 for each task. Compared to PK/PD methodologies, DN-ODE is able to predict robust individual tumor dynamics with only limited cycle data. We also introduce Principal Component Analysis visualizations for encoder results, demonstrating the DN-ODE’s capability to discern individual distributions and diverse tumor growth patterns. Therefore, DN-ODE facilitates comprehensive drug efficacy assessments, pinpoints potential responders, and aids in trial design.

2. CLEO

   Compressed Meta-Optical Encoder for Image Classification

   Anna Wirth-Singh, Jinlin Xiang, Minho Choi, and 4 more authors

   In CLEO: Fundamental Science 2024

2023

1. ICCV

   TKIL: Tangent Kernel Optimization for Class Balanced Incremental Learning

   Jinlin Xiang, and Eli Shlizerman

   In Proceedings of the IEEE/CVF International Conference on Computer Vision 2023

   Poster
2. NeurIPS DLDE

   Data-Driven Neural-ODE Modeling for Breast Cancer Tumor Dynamics and Progression-Free Survival Predictions

   Jinlin Xiang, Bozhao Qi, Qi Tang, and 2 more authors

   In The Symbiosis of Deep Learning and Differential Equations III 2023

   Poster
3. IEEE

   Driver’s Hand-Foot Coordination and Global-Regional Brain Functional Connectivity under Fatigue: Via Graph Theory and Explainable Artificial Intelligence

   Yingzhang Wu, Wenbo Li, Jie Zhang, and 4 more authors

   IEEE Transactions on Intelligent Vehicles 2023

2022

1. MICCAI-DECAF

   Incremental Learning Meets Transfer Learning: Application to Multi-site Prostate MRI Segmentation

   Chenyu You, Jinlin Xiang, Kun Su, and 5 more authors

   In Distributed, Collaborative, and Federated Learning, and Affordable AI and Healthcare for Resource Diverse Global Health 2022

   Code
2. Applied Optics

   Knowledge distillation circumvents nonlinearity for optical convolutional neural networks

   Jinlin Xiang, Shane Colburn, Arka Majumdar, and 1 more author

   Applied Optics 2022

   HTML Code

2020

1. Bi-maml: Balanced incremental approach for meta learning

   Yang Zheng, Jinlin Xiang, Kun Su, and 1 more author

   arXiv preprint arXiv:2006.07412 2020

   arXiv Bib Supp

   @article{zheng2020bi,
     title = {Bi-maml: Balanced incremental approach for meta learning},
     author = {Zheng, Yang and Xiang, Jinlin and Su, Kun and Shlizerman, Eli},
     journal = {arXiv preprint arXiv:2006.07412},
     year = {2020},
   }