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Haofeng Li

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6 papers

1 author row

AAAI Conference 2025 Conference Paper

Domain Generalized Medical Landmark Detection via Robust Boundary-Aware Pre-Training

Haifan Gong
Yu Lu
Xiang Wan
Haofeng Li

In recent years, deep learning has revenue in automated medical landmark detection. Nonetheless, prevailing research in this field predominantly addresses single-center scenarios or domain adaptation settings. In practical environments, the acquisition of multi-center data faces privacy concerns, coupled with the time-intensive and costly nature of data collection and annotation. These challenges substantially impede the broader application of deep learning-based medical landmark detection. To mitigate these issues, we propose a novel domain-generalized medical landmark detection framework that relies solely on single-center data for training. Considering the availability of numerous public medical segmentation datasets, we design a simple yet effective method that utilizes single-center segmentation to enhance the domain generalization capabilities of the landmark detection task. Specifically, we introduce a novel boundary-aware pre-training approach to focus the model on regions pertinent to landmarks. To further enhance the robustness and generalization capabilities during pre-training, we have derived a mixing loss term and proved its effectiveness in theory and practice. Extensive experiments conducted on our new domain generalization benchmark for medical landmark detection demonstrate the superiority of our approach.

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JBHI Journal 2025 Journal Article

Fetal Cerebellum Landmark Detection Based on 3D MRI: Method and Benchmark

Haifan Gong
Huixian Liu
Yitao Wang
Xiaoling Liu
Xiang Wan
Qiao Shi
Haofeng Li

Fetal cerebellum landmark detection is crucial for assessing fetal brain development. Although deep learning has become the standard for automatic landmark detection, most previous methods have focused on using 2D ultrasound or thick Magnetic Resonance Imaging (MRI). To improve accuracy, landmarks should be located on thin 3D MRIs. However, abnormal development, high noise, and fuzzy boundaries in 3D fetal brain images make traditional methods less effective for cerebellum landmark detection. To address this, we introduce the Anatomical Pseudo-label Guided Attention (APGA) network alongside a 3D MRI-based benchmark for fetal cerebellum landmark detection. During training, we use a shared encoder to extract image features and two decoders for landmark regression and anatomical pseudo-label segmentation. We design a Feature Decoupling Transformer (FDT) and embed it into the encoder to better calibrate the features for the two tasks. We only need the encoder, the FDT, and the landmark decoder during the inference phase. Extensive experiments on our proposed benchmark and out-of-domain test set have shown the effectiveness of our method. Our simulations also demonstrated that 3D biometrics are better than 2D biometrics.

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NeurIPS Conference 2025 Conference Paper

Intermediate Domain Alignment and Morphology Analogy for Patent-Product Image Retrieval

Haifan Gong
Xuanye Zhang
Ruifei Zhang
Yun Su
Zhuo Li
Yuhao Du
Anningzhe Gao
Xiang Wan

Recent advances in artificial intelligence have significantly impacted image retrieval tasks, yet Patent-Product Image Retrieval (PPIR) has received limited attention. PPIR, which retrieves patent images based on product images to identify potential infringements, presents unique challenges: (1) both product and patent images often contain numerous categories of artificial objects, but models pre-trained on standard datasets exhibit limited discriminative power to recognize some of those unseen objects; and (2) the significant domain gap between binary patent line drawings and colorful RGB product images further complicates similarity comparisons for product-patent pairs. To address these challenges, we formulate it as an open-set image retrieval task and introduce a comprehensive Patent-Product Image Retrieval Dataset (PPIRD) including a test set with 439 product-patent pairs, a retrieval pool of 727, 921 patents, and an unlabeled pre-training set of 3, 799, 695 images. We further propose a novel Intermediate Domain Alignment and Morphology Analogy (IDAMA) strategy. IDAMA maps both image types to an intermediate sketch domain using edge detection to minimize the domain discrepancy, and employs a Morphology Analogy Filter to select discriminative patent images based on visual features via analogical reasoning. Extensive experiments on PPIRD demonstrate that IDAMA significantly outperforms baseline methods (+7. 58 mAR) and offers valuable insights into domain mapping and representation learning for PPIR. (The PPIRD dataset is available at: \href{https: //loslorien. github. io/idama-project/}{https: //loslorien. github. io/idama-project/})

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AAAI Conference 2024 Conference Paper

Cell Graph Transformer for Nuclei Classification

Wei Lou
Guanbin Li
Xiang Wan
Haofeng Li

Nuclei classification is a critical step in computer-aided diagnosis with histopathology images. In the past, various methods have employed graph neural networks (GNN) to analyze cell graphs that model inter-cell relationships by considering nuclei as vertices. However, they are limited by the GNN mechanism that only passes messages among local nodes via fixed edges. To address the issue, we develop a cell graph transformer (CGT) that treats nodes and edges as input tokens to enable learnable adjacency and information exchange among all nodes. Nevertheless, training the transformer with a cell graph presents another challenge. Poorly initialized features can lead to noisy self-attention scores and inferior convergence, particularly when processing the cell graphs with numerous connections. Thus, we further propose a novel topology-aware pretraining method that leverages a graph convolutional network (GCN) to learn a feature extractor. The pre-trained features may suppress unreasonable correlations and hence ease the finetuning of CGT. Experimental results suggest that the proposed cell graph transformer with topology-aware pretraining significantly improves the nuclei classification results, and achieves the state-of-the-art performance. Code and models are available at https://github.com/lhaof/CGT

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AAAI Conference 2024 Conference Paper

UniCell: Universal Cell Nucleus Classification via Prompt Learning

Junjia Huang
Haofeng Li
Xiang Wan
Guanbin Li

The recognition of multi-class cell nuclei can significantly facilitate the process of histopathological diagnosis. Numerous pathological datasets are currently available, but their annotations are inconsistent. Most existing methods require individual training on each dataset to deduce the relevant labels and lack the use of common knowledge across datasets, consequently restricting the quality of recognition. In this paper, we propose a universal cell nucleus classification framework (UniCell), which employs a novel prompt learning mechanism to uniformly predict the corresponding categories of pathological images from different dataset domains. In particular, our framework adopts an end-to-end architecture for nuclei detection and classification, and utilizes flexible prediction heads for adapting various datasets. Moreover, we develop a Dynamic Prompt Module (DPM) that exploits the properties of multiple datasets to enhance features. The DPM first integrates the embeddings of datasets and semantic categories, and then employs the integrated prompts to refine image representations, efficiently harvesting the shared knowledge among the related cell types and data sources. Experimental results demonstrate that the proposed method effectively achieves the state-of-the-art results on four nucleus detection and classification benchmarks. Code and models are available at https://github.com/lhaof/UniCell

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AAAI Conference 2019 Conference Paper

Non-Local Context Encoder: Robust Biomedical Image Segmentation against Adversarial Attacks

Xiang He
Sibei Yang
Guanbin Li
Haofeng Li
Huiyou Chang
Yizhou Yu

Recent progress in biomedical image segmentation based on deep convolutional neural networks (CNNs) has drawn much attention. However, its vulnerability towards adversarial samples cannot be overlooked. This paper is the first one that discovers that all the CNN-based state-of-the-art biomedical image segmentation models are sensitive to adversarial perturbations. This limits the deployment of these methods in safety-critical biomedical fields. In this paper, we discover that global spatial dependencies and global contextual information in a biomedical image can be exploited to defend against adversarial attacks. To this end, non-local context encoder (NLCE) is proposed to model short- and longrange spatial dependencies and encode global contexts for strengthening feature activations by channel-wise attention. The NLCE modules enhance the robustness and accuracy of the non-local context encoding network (NLCEN), which learns robust enhanced pyramid feature representations with NLCE modules, and then integrates the information across different levels. Experiments on both lung and skin lesion segmentation datasets have demonstrated that NLCEN outperforms any other state-of-the-art biomedical image segmentation methods against adversarial attacks. In addition, NLCE modules can be applied to improve the robustness of other CNN-based biomedical image segmentation methods.

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