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Yan Fu

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5 papers

1 author row

EAAI Journal 2025 Journal Article

A consistency regularization-based approach integrating anatomical structural relationships and organ category representations for multi-organ segmentation in pigs

Xiang Pan
Hang Fan
Jianlan Wang
Yan Fu
Wei Chu
Weipeng Tai
Jing Gu
Jianming Ni

In modern biomedical research and livestock management, accurate multi-organ segmentation in pigs is essential for breeding programs. However, current methods face challenges due to low imaging contrast, size disparities, and organ shape variability. Additionally, the manual annotation of computed tomography (CT) scans is labor-intensive and costly, limiting available labeled samples. To address these issues, we propose a consistency regularization-based network guided by anatomical structural relationships and global organ category representations, specifically designed for multi-organ segmentation using a limited number of annotated CT scan samples from pigs. Specifically, we designed the SpatialLink Gated Recurrent Unit (GRU) module to extract anatomical structural information and capture dynamic spatial relationships between organs, thereby minimizing segmentation biases caused by organ shape variations. Moreover, we developed the Organ Category Coding module and Guidance module, which integrate consistency regularization and attention mechanisms, enabling the network to accurately extract global organ category representations during the decoding phase, even with a small number of labeled samples, significantly improving segmentation consistency across organs of different sizes. Additionally, We are the first to apply the Visual State Space block to multi-organ segmentation in pigs, using it to extract contextual information. Experiments on 60 pigs demonstrate that our method achieves state-of-the-art results, with significant improvements in segmentation accuracy for the gallbladder and bladder, including a 9. 8% and 4. 2% Dice score increase, respectively, and a 12. 4% and 6. 2% boost in Jaccard scores compared to compared with a selection of published methods.

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EAAI Journal 2024 Journal Article

Explicitly learning augmentation invariance for image classification by Consistent Augmentation

Xiaosong Li
Yanxia Wu
Chuheng Tang
Yan Fu
Lidan Zhang

Data augmentation is a powerful and widely used technique to improve the generalization of convolutional neural networks. The majority of data augmentation methods emphasize transforming samples to bolster network robustness. However, these methods overlook learning the invariance between the samples augmented by different transformations, impairing the quality of learned representations and generalization of the model. In this paper, we introduce a straightforward yet highly effective method called Consistent Augmentation (CA) to compel networks to learn explicitly the invariance of transformations. Specifically, CA minimizes the bidirectional KL divergence between the predicted distributions of different variants generated from the same samples using different transformations, explicitly enforcing consistency among various semantics-preserving transformations. Implementing CA is intuitive, uncomplicated, and does not introduce additional computational costs during inference. The validation experiments performed on conventional and fine-grained classification tasks demonstrate that CA can be employed across various network architectures and consistently improve their generalization ability. Notably, convolutional neural networks benefit significantly on fine-grained classification tasks, with an improvement of 5. 18% and 6. 61% compared with other augmentation methods in accuracy observed for the ShuffleNetV2 model on the Stanford Dogs and CUB-200-2011 datasets. Compatibility experiments conducted on the CIFAR-100 dataset reveal that CA improves the accuracy of the Mixup and CutMix methods by 1. 25% and 2. 11%, respectively. These results underscore the effectiveness of our approach when employed alongside mix-based augmentation methods, further enhancing the capabilities of the model.

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AAAI Conference 2021 Conference Paper

Reinforced Multi-Teacher Selection for Knowledge Distillation

Fei Yuan
Linjun Shou
Jian Pei
Wutao Lin
Ming Gong
Yan Fu
Daxin Jiang

In natural language processing (NLP) tasks, slow inference speed and huge footprints in GPU usage remain the bottleneck of applying pre-trained deep models in production. As a popular method for model compression, knowledge distillation transfers knowledge from one or multiple large (teacher) models to a small (student) model. When multiple teacher models are available in distillation, the state-of-the-art methods assign a fixed weight to a teacher model in the whole distillation. Furthermore, most of the existing methods allocate an equal weight to every teacher model. In this paper, we observe that, due to the complexity of training examples and the differences in student model capability, learning differentially from teacher models can lead to better performance of student models distilled. We systematically develop a reinforced method to dynamically assign weights to teacher models for different training instances and optimize the performance of student model. Our extensive experimental results on several NLP tasks clearly verify the feasibility and effectiveness of our approach.

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NeurIPS Conference 2021 Conference Paper

Variational Model Inversion Attacks

Kuan-Chieh Wang
Yan Fu
Ke Li
Ashish Khisti
Richard Zemel
Alireza Makhzani

Given the ubiquity of deep neural networks, it is important that these models do not reveal information about sensitive data that they have been trained on. In model inversion attacks, a malicious user attempts to recover the private dataset used to train a supervised neural network. A successful model inversion attack should generate realistic and diverse samples that accurately describe each of the classes in the private dataset. In this work, we provide a probabilistic interpretation of model inversion attacks, and formulate a variational objective that accounts for both diversity and accuracy. In order to optimize this variational objective, we choose a variational family defined in the code space of a deep generative model, trained on a public auxiliary dataset that shares some structural similarity with the target dataset. Empirically, our method substantially improves performance in terms of target attack accuracy, sample realism, and diversity on datasets of faces and chest X-ray images.

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TCS Journal 2013 Journal Article

Hamiltonian connectivity of restricted hypercube-like networks under the conditional fault model

Qiang Dong
Junlin Zhou
Yan Fu
Hui Gao

Restricted hypercube-like networks (RHLNs) are an important class of interconnection networks for parallel computing systems, which include most popular variants of the hypercubes, such as crossed cubes, Möbius cubes, twisted cubes and locally twisted cubes. This paper deals with the fault-tolerant hamiltonian connectivity of RHLNs under the conditional fault model. Let G be an n -dimensional RHLN and F ⊆ V ( G ) ⋃ E ( G ), where n ≥ 7 and ∣ F ∣ ≤ 2 n − 10. We prove that for any two nodes u, v ∈ V ( G − F ) satisfying a simple necessary condition on neighbors of u and v, there exists a hamiltonian or near-hamiltonian path between u and v in G − F. The result extends further the fault-tolerant graph embedding capability of RHLNs.

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