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Jingwen Cai

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2 papers

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EAAI Journal 2025 Journal Article

Mutual Information Guided Invertible Image Hiding Network

Kehan Zhang
Fen Xiao
Jingwen Cai
Xieping Gao

Image hiding techniques are commonly used for secure communication, copyright protection, and visual privacy. Invertible neural network (INN) have emerged as a promising approach for image steganography, enabling the concealment and recovery of secret images through forward and backward mappings within the network. However, existing methods often face limitations in the accuracy of recovered images due to challenges in estimating the lost information during the forward process. To address this issue, we propose a Mutual Information Guided Invertible Image Hiding Network (MIGIIHNet), which leverages mutual information estimation between the lost information and the stego image in the forward process to guide the backward mapping for reconstruction. Specifically, we propose a lightweight INN with a channel attention feature aggregation module (CAFAM), integrating a channel attention mechanism to optimize the multi-scale aggregation of both low-level and high-level features in a single forward pass. Also, an association learning module (ALM) is designed to model the mutual information between the stego image and the lost information during the forward hiding process. Then, the mutual information is utilized to reconstruct the secret image with high accuracy. Extensive experimental results show that MIGIIHNet outperforms existing state-of-the-art methods in terms of invisibility, security, and recovery accuracy, while maintaining low computational complexity.

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EAAI Journal 2025 Journal Article

Pseudo-label attention-based multiple instance learning for whole slide image classification

Jing He
Ping Wang
Jingwen Cai
Dan Tang
Shaowen Yao
Renyang Liu

Automating disease classification in whole slide images (WSIs) is crucial for improving clinical diagnostic efficiency. However, existing multiple instance learning (MIL) approaches for this task often struggle with challenges such as insufficient focus on positive regions and data imbalance between positive and negative regions. These issues can lead to suboptimal performance in practical applications. To address these problems, in this paper, we propose a novel embedding-based MIL technique called pseudo-label attention-based multiple instance learning (PAMIL). PAMIL aggregates each instance’s features regarding their contributions to improving downstream classification performance. The key insight of PAMIL involves training the model in a supervised manner by introducing pseudo-labels to emphasize positive regions. Additionally, we propose a fine-tuning strategy to effectively refine the dataset, eliminating the interference of false-positive data and alleviating data imbalance. The effectiveness of PAMIL was demonstrated through comparisons with six state-of-the-art MIL techniques across two large-scale, real-world datasets. Empirical results show that the proposed method outperforms other methods, achieving up to a 2. 15% improvement in accuracy and a 1. 61% increase in area under the curve (AUC) on the Cancer Genome Atlas Non-Small Cell Lung Cancer (TCGA-NSCLC) dataset, highlighting the superiority of our method in practical applications, such as helping clinicians diagnose quickly.

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