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Xiaoyuan Luo

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6 papers

1 author row

EAAI Journal 2026 Journal Article

Detection of dummy data injection attacks by using particle swarm optimization-attention temporal graph convolutional network model in power system

Xinyu Wang
Yifan Geng
Xiaoyuan Luo
Xinping Guan

As a novel deceptive topology attack, the dummy data injection attack (DDIA) poses a critical threat to power system security by exploiting both physical consistency and statistical mimicry of normal operational data, thereby evading traditional distance-based detection methods. Operating under the assumptions of a fully observable Direct Current power flow model with known topology and Gaussian measurement noise, DDIA can induce multi-line overloads while blending into legitimate measurement streams. To address this, this paper proposes a particle swarm optimization-attention temporal graph convolutional network (PSO-ATGCN) framework. The key innovation lies in a synergistic detection paradigm that integrates PSO for adversarial optimization with a topology-aware ATGCN for spatio-temporal feature extraction, specifically designed to counter the stealthy nature of DDIA: a multi-line overload DDIA model that simulates topology-driven attack scenarios; an ATGCN to dynamically capture the complex spatial-temporal dependencies inherent in grid topology and measurements; and a PSO module that concurrently tunes hyperparameters and selects critical features to enhance the model's robustness and attack-discriminative power. Experimental evaluations on IEEE 30-bus, 118-bus, and 300-bus systems demonstrate that the proposed method achieves at least 95. 21% detection accuracy under multi-line overload attacks, with lower false positive rates in noisy environments. Ablation studies confirm that the PSO component contributes a 1. 12% performance gain through feature optimization, while robustness tests validate the framework's superiority against adaptive attacks.

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NeurIPS Conference 2024 Conference Paper

FAST: A Dual-tier Few-Shot Learning Paradigm for Whole Slide Image Classification

Kexue Fu
Xiaoyuan Luo
Linhao Qu
Shuo Wang
Ying Xiong
Ilias Maglogiannis
Longxiang Gao
Manning Wang

The expensive fine-grained annotation and data scarcity have become the primary obstacles for the widespread adoption of deep learning-based Whole Slide Images (WSI) classification algorithms in clinical practice. Unlike few-shot learning methods in natural images that can leverage the labels of each image, existing few-shot WSI classification methods only utilize a small number of fine-grained labels or weakly supervised slide labels for training in order to avoid expensive fine-grained annotation. They lack sufficient mining of available WSIs, severely limiting WSI classification performance. To address the above issues, we propose a novel and efficient dual-tier few-shot learning paradigm for WSI classification, named FAST. FAST consists of a dual-level annotation strategy and a dual-branch classification framework. Firstly, to avoid expensive fine-grained annotation, we collect a very small number of WSIs at the slide level, and annotate an extremely small number of patches. Then, to fully mining the available WSIs, we use all the patches and available patch labels to build a cache branch, which utilizes the labeled patches to learn the labels of unlabeled patches and through knowledge retrieval for patch classification. In addition to the cache branch, we also construct a prior branch that includes learnable prompt vectors, using the text encoder of visual-language models for patch classification. Finally, we integrate the results from both branches to achieve WSI classification. Extensive experiments on binary and multi-class datasets demonstrate that our proposed method significantly surpasses existing few-shot classification methods and approaches the accuracy of fully supervised methods with only 0. 22% annotation costs. All codes and models will be publicly available on https: //github. com/fukexue/FAST.

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JBHI Journal 2024 Journal Article

Negative Instance Guided Self-Distillation Framework for Whole Slide Image Analysis

Xiaoyuan Luo
Linhao Qu
Qinhao Guo
Zhijian Song
Manning Wang

Histopathology image classification is an important clinical task, and current deep learning-based whole-slide image (WSI) classification methods typically cut WSIs into small patches and cast the problem as multi-instance learning. The mainstream approach is to train a bag-level classifier, but their performance on both slide classification and positive patch localization is limited because the instance-level information is not fully explored. In this article, we propose a negative instance-guided, self-distillation framework to directly train an instance-level classifier end-to-end. Instead of depending only on the self-supervised training of the teacher and the student classifiers in a typical self-distillation framework, we input the true negative instances into the student classifier to guide the classifier to better distinguish positive and negative instances. In addition, we propose a prediction bank to constrain the distribution of pseudo instance labels generated by the teacher classifier to prevent the self-distillation from falling into the degeneration of classifying all instances as negative. We conduct extensive experiments and analysis on three publicly available pathological datasets: CAMELYON16, PANDA, and TCGA, as well as an in-house pathological dataset for cervical cancer lymph node metastasis prediction. The results show that our method outperforms existing methods by a large margin. Code will be publicly available.

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AAAI Conference 2024 Conference Paper

Transformer-Based Video-Structure Multi-Instance Learning for Whole Slide Image Classification

Yingfan Ma
Xiaoyuan Luo
Kexue Fu
Manning Wang

Pathological images play a vital role in clinical cancer diagnosis. Computer-aided diagnosis utilized on digital Whole Slide Images (WSIs) has been widely studied. The major challenge of using deep learning models for WSI analysis is the huge size of WSI images and existing methods struggle between end-to-end learning and proper modeling of contextual information. Most state-of-the-art methods utilize a two-stage strategy, in which they use a pre-trained model to extract features of small patches cut from a WSI and then input these features into a classification model. These methods can not perform end-to-end learning and consider contextual information at the same time. To solve this problem, we propose a framework that models a WSI as a pathologist's observing video and utilizes Transformer to process video clips with a divide-and-conquer strategy, which helps achieve both context-awareness and end-to-end learning. Extensive experiments on three public WSI datasets show that our proposed method outperforms existing SOTA methods in both WSI classification and positive region detection.

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NeurIPS Conference 2023 Conference Paper

The Rise of AI Language Pathologists: Exploring Two-level Prompt Learning for Few-shot Weakly-supervised Whole Slide Image Classification

Linhao Qu
Xiaoyuan Luo
Kexue Fu
Manning Wang
Zhijian Song

This paper introduces the novel concept of few-shot weakly supervised learning for pathology Whole Slide Image (WSI) classification, denoted as FSWC. A solution is proposed based on prompt learning and the utilization of a large language model, GPT-4. Since a WSI is too large and needs to be divided into patches for processing, WSI classification is commonly approached as a Multiple Instance Learning (MIL) problem. In this context, each WSI is considered a bag, and the obtained patches are treated as instances. The objective of FSWC is to classify both bags and instances with only a limited number of labeled bags. Unlike conventional few-shot learning problems, FSWC poses additional challenges due to its weak bag labels within the MIL framework. Drawing inspiration from the recent achievements of vision-language models (V-L models) in downstream few-shot classification tasks, we propose a two-level prompt learning MIL framework tailored for pathology, incorporating language prior knowledge. Specifically, we leverage CLIP to extract instance features for each patch, and introduce a prompt-guided pooling strategy to aggregate these instance features into a bag feature. Subsequently, we employ a small number of labeled bags to facilitate few-shot prompt learning based on the bag features. Our approach incorporates the utilization of GPT-4 in a question-and-answer mode to obtain language prior knowledge at both the instance and bag levels, which are then integrated into the instance and bag level language prompts. Additionally, a learnable component of the language prompts is trained using the available few-shot labeled data. We conduct extensive experiments on three real WSI datasets encompassing breast cancer, lung cancer, and cervical cancer, demonstrating the notable performance of the proposed method in bag and instance classification. All codes will be made publicly accessible.

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NeurIPS Conference 2022 Conference Paper

Bi-directional Weakly Supervised Knowledge Distillation for Whole Slide Image Classification

Linhao Qu
Xiaoyuan Luo
Manning Wang
Zhijian Song

Computer-aided pathology diagnosis based on the classification of Whole Slide Image (WSI) plays an important role in clinical practice, and it is often formulated as a weakly-supervised Multiple Instance Learning (MIL) problem. Existing methods solve this problem from either a bag classification or an instance classification perspective. In this paper, we propose an end-to-end weakly supervised knowledge distillation framework (WENO) for WSI classification, which integrates a bag classifier and an instance classifier in a knowledge distillation framework to mutually improve the performance of both classifiers. Specifically, an attention-based bag classifier is used as the teacher network, which is trained with weak bag labels, and an instance classifier is used as the student network, which is trained using the normalized attention scores obtained from the teacher network as soft pseudo labels for the instances in positive bags. An instance feature extractor is shared between the teacher and the student to further enhance the knowledge exchange between them. In addition, we propose a hard positive instance mining strategy based on the output of the student network to force the teacher network to keep mining hard positive instances. WENO is a plug-and-play framework that can be easily applied to any existing attention-based bag classification methods. Extensive experiments on five datasets demonstrate the efficiency of WENO. Code is available at https: //github. com/miccaiif/WENO.

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