Jiabao Wen

AAAI Conference 2026 Conference Paper

Automatic Translational Correction of Multi-View Coronary Angiography Based on Auto-Annotation Data Generation

• Yue Cao
• Zhuo Zhang
• Shuai Xiao
• Jialin Li
• Guipeng Lan
• Jiabao Wen
• Jiachen Yang

Multi-view automatic translational correction (ATC) in coronary angiography (CAG) is critical for intraoperative automatic diagnosis, in which deep learning playing a key role. However, heartbeat-induced soft matching errors and costly annotations make it difficult to build high-quality, large-scale datasets for calibration algorithm training. The training of clinical models is difficult to fulfill, as existing datasets differ significantly from real CAG in both style and structure. To address this challenge, we propose a novel high-quality data synthesis method for annotation-free ATC. We fully automated the construction of a labeled, high-fidelity dataset for training matching models. An evolutionary algorithm is introduced for global optimization of translation estimation, mitigating epipolar constraint violations caused by vascular deformation and enabling reliable correction across large viewpoint differences. Furthermore, a theoretical analysis is presented, demonstrating that error propagation between adjacent views is more accurate than direct estimation across distant views. Our experiments on clinical datasets demonstrate that our method not only significantly outperforms weakly supervised learning approaches, but also performs comparably to fully supervised methods. Moreover, it exhibits remarkable multicenter generalizability.

JBHI Journal 2025 Journal Article

Generative AI-Based Data Completeness Augmentation Algorithm for Data-Driven Smart Healthcare

• Guipeng Lan
• Shuai Xiao
• Jiachen Yang
• Jiabao Wen
• Meng Xi

In the decade, artificial intelligence has achieved great popularity and applications in medicine and healthcare. Various AI-based algorithms have shown astonishing performance. However, in various data-driven smart healthcare algorithms, the problem of incomplete dataset remains a huge challenge. In this paper, we propose a data completeness enhancement algorithm based on generative AI (i. e. , GenAI-DAA) to solve the problems of the in-sufficient data for model training, the data imbalance, and the biases of the training samples. We first construct the cognitive field of the generative models and effectively understand the state of incomplete cognition in generative models. Secondly, on this basis, we propose a quest algorithm for abnormal samples in the cognitive field based on local outlier factor. By fine-grained value evaluation, abnormal samples are given more refined attention. Finally, integrating the above process through multiple cognitive adjustments, GenAI-DAA gradually improves the cognitive ability. GenAI-DAA can be summarized as “Quest $ \longrightarrow$ Estimate $ \longrightarrow$ Tune-up”. We have conducted extensive experiments to demonstrate the effectiveness of our proposed algorithm, and shown widely applications to some typical data-driven smart healthcare algorithms.

ICLR Conference 2025 Conference Paper

Inner Information Analysis Algorithm for Deep Neural Network based on Community

• Guipeng Lan
• Shuai Xiao 0001
• Meng Xi 0001
• Jiabao Wen
• Jiachen Yang

Deep learning has achieved advancements across a variety of forefront fields. However, its inherent 'black box' characteristic poses challenges to the comprehension and trustworthiness of the decision-making processes within neural networks. To mitigate these challenges, we introduce InnerSightNet, an inner information analysis algorithm designed to illuminate the inner workings of deep neural networks through the perspectives of community. This approach is aimed at deciphering the intricate patterns of neurons within deep neural networks, thereby shedding light on the networks' information processing and decision-making pathways. InnerSightNet operates in three primary phases, 'neuronization-aggregation-evaluation'. Initially, it transforms learnable units into a structured network of neurons. Subsequently, these neurons are aggregated into distinct communities according to representation attributes. The final phase involves the evaluation of these communities' roles and functionalities, to unpick the information flow and decision-making. By transcending focus on single-layer or individual neuron, InnerSightNet broadens the horizon for deep neural network interpretation. InnerSightNet offers a unique vantage point, enabling insights into the collective behavior of communities within the overarching architecture, thereby enhancing transparency and trust in deep learning systems.

EAAI Journal 2024 Journal Article

Curvature index of image samples used to evaluate the interpretability informativeness

• Zhuo Zhang
• Shuai Xiao
• Meng Xi
• Jiabao Wen
• Jiachen Yang

IS Journal 2024 Journal Article

Data-Driven Deepfake Forensics Model Based on Large-Scale Frequency and Noise Features

• Guipeng Lan
• Shuai Xiao
• Jiabao Wen
• Desheng Chen
• Yong Zhu

With the rapid development of deep learning and communication technology, the application of streaming media services and social software have gone deep into life. However, in the face of many uncertain factors in data dissemination, protecting privacy and security is particularly important. In order to solve the abovementioned problems, this study proposes a deep face forgery forensics method with frequency domain and noise features. In this method, discrete cosine transform is proposed to perceive the forgery trace features of different frequency bands in the frequency domain. At the same time, the spatial rich model is used for guidance to enhance the traces of forged noise. Then, large-scale network and single center loss function are introduced to improve the forensics ability of the model. Experimental results on several databases such as faceforensics++, celeb DF, and DFDC show that this method can effectively improve the accuracy of forensics.

AAAI Conference 2024 Conference Paper

Generative Model Perception Rectification Algorithm for Trade-Off between Diversity and Quality

• Guipeng Lan
• Shuai Xiao
• Jiachen Yang
• Jiabao Wen

How to balance the diversity and quality of results from generative models through perception rectification poses a significant challenge. Abnormal perception in generative models is typically caused by two factors: inadequate model structure and imbalanced data distribution. In response to this issue, we propose the dynamic model perception rectification algorithm (DMPRA) for generalized generative models. The core idea is to gain a comprehensive perception of the data in the generative model by appropriately highlighting the low-density samples in the perception space, also known as the minor group samples. The entire process can be summarized as "search-evaluation-adjustment". To identify low-density regions in the data manifold within the perception space of generative models, we introduce a filtering method based on extended neighborhood sampling. Based on the informational value of samples from low-density regions, our proposed mechanism generates informative weights to assess the significance of these samples in correcting the models' perception. By using dynamic adjustment, DMPRA ensures simultaneous enhancement of diversity and quality in the presence of imbalanced data distribution. Experimental results indicate that the algorithm has effectively improved Generative Adversarial Nets (GANs), Normalizing Flows (Flows), Variational Auto-Encoders (VAEs), and Diffusion Models (Diffusion).