Short Film: Comet Spleen
Logline: Two lonely women share an apartment but occupy it at different times, their presence lingering in the room like an ungraspable connection felt only through imagination. 
Posts by Collection
portfolio
Drama: An Evening of the Plum Rain Season
Adapted into an immersive environmental theatre in 2023, and performed at the Luojia Mountain Theater, Wuhan University.
project
AI Sign Language Generation in Concert (Ongoing)
Develop an AI-driven system to generate real-time American Sign Language (ASL) during concerts, providing accessibility to deaf and hard-of-hearing audiences.
Drone-to-Drone Tracking
I worked on this project when I was a Research Assistant at HKU Metaverse Innovation Lab. 
Medical Image Analysis Based on Deep Learning Methods
HKU MSc Final Year Project
publications
SPPNet: A Single-Point Prompt Network for Nuclei Image Segmentation Permalink
Published in MICCAI-MLMI, 2023
Image segmentation plays an essential role in nuclei image analysis. Recently, the segment anything model has made a significant breakthrough in such tasks. However, the current model exists two major issues for cell segmentation: (1) the image encoder of the segment anything model involves a large number of parameters. Retraining or even fine-tuning the model still requires expensive computational resources. (2) in point prompt mode, points are sampled from the center of the ground truth and more than one set of points is expected to achieve reliable performance, which is not efficient for practical applications. In this paper, a single-point prompt network is proposed for nuclei image segmentation, called SPPNet. We replace the original image encoder with a lightweight vision transformer. Also, an effective convolutional block is added in parallel to extract the low-level semantic information from the image and compensate for the performance degradation due to the small image encoder. We propose a new point-sampling method based on the Gaussian kernel. The proposed model is evaluated on the MoNuSeg-2018 dataset. The result demonstrated that SPPNet outperforms existing U-shape architectures and shows faster convergence in training. Compared to the segment anything model, SPPNet shows roughly 20 times faster inference, with 1/70 parameters and computational cost. Particularly, only one set of points is required in both the training and inference phases, which is more reasonable for clinical applications. The code for our work and more technical details can be found at this URL.
Recommended citation: Qing Xu, Wenwei Kuang, Zeyu Zhang, Xueyao Bao, Haoran Chen, and Wenting Duan (2023, October). “SPPNet: A Single-Point Prompt Network for Nuclei Image Segmentation.” In International Workshop on Machine Learning in Medical Imaging (pp. 227-236). Cham: Springer Nature Switzerland.
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talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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