Author name cluster

Jie Wei

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6 papers

2 author rows

JBHI Journal 2026 Journal Article

Whole-Process Evolutionary Heterogeneity Analysis for Glioblastoma Radiotherapy Response Prediction

Yao Zheng
Dong Huang
Jie Wei
Tianci Liu
Xiaoting Wu
Yuefei Feng
Chengwei Chen
Yang Liu

As a highly heterogeneous tumor, radiotherapy for Glioblastoma (GBM) is a complex and dynamic process. Traditional predictive methods of treatment response often rely on one or a few fixed time points, but this static approach may fail to capture the detailed, individualized changes occurring throughout the treatment process. To address these limitations, we proposed a novel approach called Evolutionary Heterogeneity Analysis Framework (EvoHAF), which integrates tumor heterogeneity and whole-process evolution of GBM radiotherapy. Our framework introduces an Image Heterogeneity Encoder, designed to capture the intricate spatial heterogeneity based on tumor subregions. Additionally, the Temporal Self-Attention Module (TSAM) mechanism integrates longitudinal imaging data throughout the course of radiotherapy, capturing the evolving nature of the tumor. We further introduce a Compensated Prediction Head (CPH) that dynamically refines predictions throughout the patient's radiotherapy. Experimental results on a cross-center cohort, including an internal dataset of 112 patients and an external validation dataset of 80 patients, demonstrate that EvoHAF achieves strong performance. For internal 5-fold validation, the AUC was 0. 8519±0. 0583, and for external validation, the AUC was 0. 7675±0. 0858. These results demonstrate the model's capability to provide accurate whole-process predictions. Moreover, the model's credibility is reinforced by providing visual explanations at both 2D and 3D subregional levels, establishing trust in its decisions and laying a strong foundation for clinical applications.

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NeurIPS Conference 2024 Conference Paper

Efficient Temporal Action Segmentation via Boundary-aware Query Voting

Peiyao Wang
Yuewei Lin
Erik Blasch
Jie Wei
Haibin Ling

Although the performance of Temporal Action Segmentation (TAS) has been improved in recent years, achieving promising results often comes with a high computational cost due to dense inputs, complex model structures, and resource-intensive post-processing requirements. To improve the efficiency while keeping the high performance, we present a novel perspective centered on per-segment classification. By harnessing the capabilities of Transformers, we tokenize each video segment as an instance token, endowed with intrinsic instance segmentation. To realize efficient action segmentation, we introduce BaFormer, a boundary-aware Transformer network. It employs instance queries for instance segmentation and a global query for class-agnostic boundary prediction, yielding continuous segment proposals. During inference, BaFormer employs a simple yet effective voting strategy to classify boundary-wise segments based on instance segmentation. Remarkably, as a single-stage approach, BaFormer significantly reduces the computational costs, utilizing only 6% of the running time compared to the state-of-the-art method DiffAct, while producing better or comparable accuracy over several popular benchmarks. The code for this project is publicly available at https: //github. com/peiyao-w/BaFormer.

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JBHI Journal 2024 Journal Article

Multi-View Cross-Fusion Transformer Based on Kinetic Features for Non-Invasive Blood Glucose Measurement Using PPG Signal

Shisen Chen
Fen Qin
Xuesheng Ma
Jie Wei
Yuan-Ting Zhang
Yuan Zhang
Emil Jovanov

Noninvasive blood glucose (BG) measurement could significantly improve the prevention and management of diabetes. In this paper, we present a robust novel paradigm based on analyzing photoplethysmogram (PPG) signals. The method includes signal pre-processing optimization and a multi-view cross-fusion transformer (MvCFT) network for non-invasive BG assessment. Specifically, a multi-size weighted fitting (MSWF) time-domain filtering algorithm is proposed to optimally preserve the most authentic morphological features of the original signals. Meanwhile, the spatial position encoding-based kinetics features are reconstructed and embedded as prior knowledge to discern the implicit physiological patterns. In addition, a cross-view feature fusion (CVFF) module is designed to incorporate pairwise mutual information among different views to adequately capture the potential complementary features in physiological sequences. Finally, the subject-wise 5-fold cross-validation is performed on a clinical dataset of 260 subjects. The root mean square error (RMSE) and mean absolute error (MAE) of BG measurements are 1. 129 mmol/L and 0. 659 mmol/L, respectively, and the optimal Zone A in the Clark error grid, representing none clinical risk, is 87. 89%. The results indicate that the proposed method has great potential for homecare applications.

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IROS Conference 2023 Conference Paper

Transparent Object Tracking with Enhanced Fusion Module

Kalyan Garigapati
Erik Blasch
Jie Wei
Haibin Ling

Accurate tracking of transparent objects, such as glasses, plays a critical role in many robotic tasks such as robot-assisted living. Due to the adaptive and often reflective texture of such objects, traditional tracking algorithms that rely on general-purpose learned features suffer from reduced performance. Recent research has proposed to instill trans-parency awareness into existing general object trackers by fusing purpose-built features. However, with the existing fusion techniques, the addition of new features causes a change in the latent space making it impossible to incorporate transparency awareness on trackers with fixed latent spaces. For example, many of the current days' transformer-based trackers are fully pre-trained and are sensitive to any latent space perturbations. In this paper, we present a new feature fusion technique that integrates transparency information into a fixed feature space, enabling its use in a broader range of trackers. Our proposed fusion module, composed of a transformer encoder and an MLP module, leverages key query-based transformations to embed the transparency information into the tracking pipeline. We also present a new two-step training strategy for our fusion module to effectively merge transparency features. We propose a new tracker architecture that uses our fusion techniques to achieve superior results for transparent object tracking. Our proposed method achieves competitive results with state-of-the-art trackers on TOTB, which is the largest transparent object tracking benchmark recently released. Our results and the implementation of code will be made publicly available at https://github.com/kalyan0510/TOTEM.

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IROS Conference 1999 Conference Paper

On active camera control with foveate wavelet transform

Jie Wei
Ze-Nian Li

In this paper, a new variable resolution technique, foveate wavelet transform (FWT), is introduced to represent video frames in an effort to emulate the animate vision systems. Compared to the existing variable resolution techniques, the strength of the proposed scheme encompasses its flexibility and conciseness while supporting interesting behavior resembling the animate vision system. With the FWT as the representation, an efficient scheme to achieve purposive vision is developed. The foveate potential motion area is first labeled, the camera motion is then determined by following the labeled moving object. Experiments based on our computer-controlled pan-tilt-zoom camera have demonstrated its efficacy in real-time active camera control.

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IROS Conference 1998 Conference Paper

Efficient disparity-based gaze control with foveate wavelet transform

Jie Wei
Ze-Nian Li

A new variable resolution technique-foveate wavelet transform (FWT)-is proposed in this paper to represent images in an effort to emulate the animate visual systems. With the FWT representation and a simplified configuration of the stereo rig, efficient disparity-based algorithms to achieve gaze control of the rig are developed. With these algorithms, the attention of the stereo rig can be centered around the object of most interest with desired resolution for both the static and moving objects. Experiments based on the algorithms have demonstrated their efficacies in the gaze control of the stereo rig.

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