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Yilei Shi

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3 papers

2 author rows

AAAI Conference 2026 Conference Paper

ProPL: Universal Semi-Supervised Ultrasound Image Segmentation via Prompt-Guided Pseudo-Labeling

Yaxiong Chen
Qicong Wang
Chunlei Li
Jingliang Hu
Yilei Shi
Shengwu Xiong
Xiao Xiang Zhu
Lichao Mou

Existing approaches for the problem of ultrasound image segmentation, whether supervised or semi-supervised, are typically specialized for specific anatomical structures or tasks, limiting their practical utility in clinical settings. In this paper, we pioneer the task of universal semi-supervised ultrasound image segmentation and propose ProPL, a framework that can handle multiple organs and segmentation tasks while leveraging both labeled and unlabeled data. At its core, ProPL employs a shared vision encoder coupled with prompt-guided dual decoders, enabling flexible task adaptation through a prompting-upon-decoding mechanism and reliable self-training via an uncertainty-driven pseudo-label calibration (UPLC) module. To facilitate research in this direction, we introduce a comprehensive ultrasound dataset spanning 5 organs and 8 segmentation tasks. Extensive experiments demonstrate that ProPL outperforms state-of-the-art methods across various metrics, establishing a new benchmark for universal ultrasound image segmentation.

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AAAI Conference 2025 Conference Paper

LPCG: A Self-conditional Architecture for Labeled Point Cloud Generation

Dongshuo Huang
Xiaoshui Huang
Chengdong Zhang
Yilei Shi

Recently, there has been considerable exploration of methods for generating 3D point clouds, which is crucial for numerous 3D vision applications. Though conditional generation methods show promising performance, it depends on the additional paired label. On the other hand, unconditional generation methods usually fail to annotate the generated 3D point cloud. In this paper, we introduce a novel self-conditional architecture that trains on unlabeled data and then generates high-quality labeled 3D point clouds. Specifically, we design a module to extract geometry and view features, and then use a feature fusion module to integrate them as a substitute for label embedding in conditional point cloud generation. Then the point cloud generator is trained using the fused features. LPCG also harnesses CLIP to handle the view features of point clouds for generating label information. Besides, we train two feature diffusion modules to capture the essence of multimodal features and obtain diverse fused features for use as conditions in generating point clouds. Experiments on the ShapeNet dataset demonstrate that LPCG achieves state-of-the-art performance for single class generation. Our experimental results show that the accuracy of our generated label annotations reaches around 97.44% for a two-class generation task.

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ICLR Conference 2025 Conference Paper

Scale-Aware Contrastive Reverse Distillation for Unsupervised Medical Anomaly Detection

Chunlei Li
Yilei Shi
Jingliang Hu
Xiao Xiang Zhu 0001
Lichao Mou

Unsupervised anomaly detection using deep learning has garnered significant research attention due to its broad applicability, particularly in medical imaging where labeled anomalous data are scarce. While earlier approaches leverage generative models like autoencoders and generative adversarial networks (GANs), they often fall short due to overgeneralization. Recent methods explore various strategies, including memory banks, normalizing flows, self-supervised learning, and knowledge distillation, to enhance discrimination. Among these, knowledge distillation, particularly reverse distillation, has shown promise. Following this paradigm, we propose a novel scale-aware contrastive reverse distillation model that addresses two key limitations of existing reverse distillation methods: insufficient feature discriminability and inability to handle anomaly scale variations. Specifically, we introduce a contrastive student-teacher learning approach to derive more discriminative representations by generating and exploring out-of-normal distributions. Further, we design a scale adaptation mechanism to softly weight contrastive distillation losses at different scales to account for the scale variation issue. Extensive experiments on benchmark datasets demonstrate state-of-the-art performance, validating the efficacy of the proposed method. The code will be made publicly available.

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