Author name cluster

Yan Qiang

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

6 papers

1 author row

AAAI Conference 2026 Conference Paper

Augmentation-invariant Learning Strategy via Data Augmentation for Improving Model Generalization

Yu Miao
Juanjuan Zhao
Sijie Song
Ran Gong
Yuanqian Zhu
Lusha Qi
Yan Qiang

Data augmentation is an effective technique for regularizing deep networks, which helps to enhance the generalizability and robustness of the model. However, in the field of medical imaging, traditional data augmentation techniques such as cropping, rotation, and degradation may inadvertently alter the critical characteristics of pathological lesions. Conventional semantic augmentation methods, such as altering the color and contrast of the object background, may also affect the structural features of medical images in uncontrolled semantic directions. Such operational conditions compromise the model's diagnostic reliability in medical contexts. To address this issue, we propose a surprisingly efficient implicit augmentation-invariant learning strategy (AILS) via variational Bayesian inference on differentially constrained feature manifolds. Parameterizing probability measures over tangent space through deep networks enables precise estimation of semantic direction distributions. Subsequently, geodesic-aware semantic features are sampled from the reparameterized variational posterior, achieving semantic-consistent augmentation. Simultaneously, to mine augmentation distribution invariance, we design the AiHLoss, which constrains the augmentation distribution to facilitate the network to learn augmentation invariance. Extensive experiments demonstrate that AILS exhibits high performance on public medical image datasets, outperforming existing augmentation methods.

PDF Details DOI

EAAI Journal 2025 Journal Article

Frequency domain nuances guided parallel transformer model for industrial anomaly localization

Jun Zhao
Kaixuan Yu
Yu Miao
Yingsen Wang
Yue Ma
Jiawei Zhang
Juanjuan Zhao
Yan Qiang

Unsupervised visual anomaly localization research has garnered significant attention in industrial component surface quality inspection tasks, particularly in realistic scenarios characterized by extreme imbalance between positive and negative samples. While several existing approaches endeavor to design hybrid architectures of convolutional neural networks (CNNs) and vision transformers (ViTs) to enhance the performance capability of unsupervised models, their overall effectiveness remains unstable. In this paper, we introduce a novel approach known as frequency domain nuances mining (FDNM), presenting a parallel transformer framework for anomaly localization. This framework leverages the inductive bias property of CNNs for local spatial understanding and the advantageous ViTs for global representation learning, enabling simultaneous capture of local correlations and global information. To further enhance the model’s discriminative ability for subtle differences, we propose a frequency domain decoupling mechanism that exploits the frequency domain properties of images to improve model interpretability. Specifically, multi-granularity differences among frequency domain components serve as prior embeddings in FDNM, augmenting the potential feature representation of local details and global semantics. Furthermore, we synergistically train frequency domain invariant sampling loss and focal loss to balance the differential representation of features in the cross-frequency domain, leading to a more stable training process and more accurate anomaly localization results. Experiments conducted on the challenging 15-class industrial anomaly detection dataset validate the superiority of the proposed method. The image-level area under the receiver operating characteristic curve (AUROC), pixel-level AUROC and average precision (AP) scores of FDNM reach 99. 0%, 98. 7% and 68. 7%, respectively, demonstrating comparable performance to state-of-the-art methods.

Details DOI

JBHI Journal 2025 Journal Article

Learning Consistent Semantic Representation for Chest X-ray via Anatomical Localization in Self-Supervised Pre-Training

Surong Chu
Xueting Ren
Guohua Ji
Juanjuan Zhao
Jinwei Shi
Yangyang Wei
Bo Pei
Yan Qiang

Despite the similar global structures in Chest X-ray (CXR) images, the same anatomy exhibits varying appearances across images, including differences in local textures, shapes, colors, etc. Learning consistent representations for anatomical semantics through these diverse appearances poses a great challenge for self-supervised pre-training in CXR images. To address this challenge, we propose two new pre-training tasks: i nner- i mage a natomy l ocalization (IIAL) and c ross- i mage a natomy l ocalization (CIAL). Leveraging the relatively stable positions of identical anatomy across images, we utilize position information directly as supervision to learn consistent semantic representations. Specifically, IIAL adopts a coarse-to-fine heatmap localization approach to correlate anatomical semantics with positions, while CIAL leverages feature affine alignment and heatmap localization to establish a correspondence between identical anatomical semantics across varying images, despite their appearance diversity. Furthermore, we introduce a unified end-to-end pre-training framework, a natomy- a ware r epresentation l earning (AARL), integrating IIAL, CIAL, and a pixel restoration task. The advantages of AARL are: 1) preserving the appearance diversity and 2) training in a simple end-to-end way avoiding complicated preprocessing. Extensive experiments on six downstream tasks, including classification and segmentation tasks in various application scenarios, demonstrate that our AARL: 1) has more powerful representation and transferring ability; 2) is annotation-efficient, reducing the demand for labeled data and 3) improves the sensitivity to detecting various pathological and anatomical patterns.

Details DOI

AIIM Journal 2024 Journal Article

CHNet: A multi-task global–local Collaborative Hybrid Network for KRAS mutation status prediction in colorectal cancer

Meiling Cai
Lin Zhao
Yan Qiang
Long Wang
Juanjuan Zhao

Accurate prediction of Kirsten rat sarcoma (KRAS) mutation status is crucial for personalized treatment of advanced colorectal cancer patients. However, despite the excellent performance of deep learning models in certain aspects, they often overlook the synergistic promotion among multiple tasks and the consideration of both global and local information, which can significantly reduce prediction accuracy. To address these issues, this paper proposes an innovative method called the Multi-task Global–Local Collaborative Hybrid Network (CHNet) aimed at more accurately predicting patients’ KRAS mutation status. CHNet consists of two branches that can extract global and local features from segmentation and classification tasks, respectively, and exchange complementary information to collaborate in executing these tasks. Within the two branches, we have designed a Channel-wise Hybrid Transformer (CHT) and a Spatial-wise Hybrid Transformer (SHT). These transformers integrate the advantages of both Transformer and CNN, employing cascaded hybrid attention and convolution to capture global and local information from the two tasks. Additionally, we have created an Adaptive Collaborative Attention (ACA) module to facilitate the collaborative fusion of segmentation and classification features through guidance. Furthermore, we introduce a novel Class Activation Map (CAM) loss to encourage CHNet to learn complementary information between the two tasks. We evaluate CHNet on the T2-weighted MRI dataset, and achieve an accuracy of 88. 93% in KRAS mutation status prediction, which outperforms the performance of representative KRAS mutation status prediction methods. The results suggest that our CHNet can more accurately predict KRAS mutation status in patients via a multi-task collaborative facilitation and considering global–local information way, which can assist doctors in formulating more personalized treatment strategies for patients.

Details DOI

EAAI Journal 2023 Journal Article

Deep learning approach for predicting lymph node metastasis in non-small cell lung cancer by fusing image–gene data

Guojie Hou
Liye Jia
Yanan Zhang
Wei Wu
Lin Zhao
Juanjuan Zhao
Long Wang
Yan Qiang

The determination of lymph node metastasis is critical to the selection of treatment options for non-small cell lung cancer. Invasive pathological examinations cannot be performed frequently in clinical practice, thus non-invasive and reproducible methods are needed. The current research on non-invasive prediction methods based on image and genetic information has shortcomings such as small data sample size, high data dimension, and poor multimodal fusion effect. In this research, we propose a method for predicting lymph node metastasis in non-small cell lung cancer by fusing imaging data and genetic data to overcome these challenges. An attention-based multimodal information fusion module is designed to fuse image data and genetic data in the mid-fusion, and a bilinear fusion module based on Tucker decomposition is inserted into the model for late fusion, which significantly improves the performance of multimodal fusion. The 3D spiral transformation method is used to extract 2D images from 3D data, and the transformed images inherit and retain the spatial correlation of the original texture and edge information while increasing the image data sample size for subsequent prediction. The random forest method of important measurement is used for feature selection, and redundant data in gene information is eliminated. The experiments are carried out on the NSCLC-Radiogenomics dataset. The accuracy and AUC of the proposed model are 0. 968 and 0. 963, respectively. The experimental results show that the model is ideal performance in predicting lymph node metastasis, providing a new method for non-invasive lymph node metastasis prediction, which is beneficial to the application of precision medicine.

Details DOI

EAAI Journal 2021 Journal Article

Integrate domain knowledge in training multi-task cascade deep learning model for benign–malignant thyroid nodule classification on ultrasound images

Wenkai Yang
Yunyun Dong
Qianqian Du
Yan Qiang
Kun Wu
Juanjuan Zhao
Xiaotang Yang
Muhammad Bilal Zia

The automatic and accurate diagnosis of thyroid nodules in ultrasound images is of great significance to reduce the workload and radiologists’ misdiagnosis rate. Although deep learning has shown strong image classification performance, the inherent limitations of medical images small dataset and time-consuming access to lesion annotations, leaving this work still facing challenges. In our study, a multi-task cascade deep learning model (MCDLM) was proposed, which integrates radiologists’ various domain knowledge (DK) and uses multimodal ultrasound images for automatic diagnosis of thyroid nodules. Specifically, we transfer the knowledge learned by U-net from the source domain to the target domain under the guidance of radiologist’ marks to obtain more accurate nodules’ segmentation results. We then quantify the nodules’ ultrasound features (UF) as conditions to assist the dual-path semi-supervised conditional generative adversarial network (DScGAN) in generating higher quality images obtaining more powerful discriminators. After that, we concatenate DScGAN learning’s image representation to train a supervised support vector machine (S3VM) for thyroid nodule classification. The experiment results on ultrasound images of 1030 patients suggest that the MCDLM model can achieve almost the same classification performance as the fully supervised learning (an accuracy of 90. 01% and an AUC of 91. 07%) using only about 35% of the full labeled dataset, which saves a lot of time and effort compared to traditional methods.

Details DOI