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Weipeng Cao

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3 papers

1 author row

EAAI Journal 2024 Journal Article

Self-supervised adversarial adaptation network for breast cancer detection

Mahnoosh Torabi
Amir Hosein Rasouli
Q.M. Jonathan Wu
Weipeng Cao
Farhad Pourpanah

Breast cancer is the most commonly diagnosed cancer worldwide, and early detection is essential for reducing mortality rates. Digital mammography is currently the best standard for early detection, as it can assist physicians in treating the disease. However, inaccurate diagnoses from mammography are common and can lead to patients undergoing unnecessary tests and treatments. To address this challenge, deep-learning techniques have shown promising results in improving the accuracy and reliability of breast cancer detection. However, existing methods face two primary challenges: the lack of the annotated data, and the inability to adapt to new data domains. In this paper, we propose SelfAdaptNet to address these issues. Specifically, SelfAdaptNet employs self-supervised learning techniques, such as Bootstrap Your Own Latent (BYOL) and Simple Framework for Learning of Visual Representations (SimCLR), to tackle the problem of limited annotated data. Additionally, the adversarial technique is used to address the problem of domain shift. By successfully reducing domain disparities, this strategy enhances the model’s adaptability and robustness across a variety of clinical scenarios. Overall, our contributions offer a more effective and flexible approach for early breast cancer detection, and experimental results demonstrate that SelfAdaptNet can produce promising results as compared with other methods.

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EAAI Journal 2023 Journal Article

AdaDerivative optimizer: Adapting step-sizes by the derivative term in past gradient information

Weidong Zou
Yuanqing Xia
Weipeng Cao

AdaBelief fully utilizes “belief” to iteratively update the parameters of deep neural networks. However, the reliability of the “belief” is determined by the gradient’s prediction accuracy, and the key to this prediction accuracy is the selection of the smoothing parameter β 1. AdaBelief also suffers from the overshoot problem, which occurs when the value of parameters exceeds the value of the target and cannot be changed along the gradient direction. In this paper, we propose AdaDerivative to eliminate the overshoot problem of AdaBelief. The key to AdaDerivative is that the “belief” of AdaBelief is replaced by the derivative term’s exponential moving average (EMA), which can be constructed as ( 1 − β 2 ) ∑ i = 1 t β 2 t − i ( g i − g i − 1 ) 2 based on the past and current gradients. We validate the performance of AdaDerivative on a variety of tasks, including image classification, language modeling, node classification, image generation, and object detection tasks. Extensive experimental results demonstrate that AdaDerivative can achieve state-of-the-art performance.

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EAAI Journal 2022 Journal Article

Broad learning system based on driving amount and optimization solution

Weidong Zou
Yuanqing Xia
Weipeng Cao

Broad learning system (BLS) was proposed by C. L. Philip Chen to overcome the time-consuming problem of traditional deep learning. However, the prediction precision of BLS is mainly dependent on its regularized parameter λ. Usually, λ is calculated by the trial and error method, which often suffers from the problem of too much calculation. To alleviate this issue, we propose an improved BLS with the driving amount and optimization solution (i. e. , DA-BLS) in the study. The contributions of this study include: First, we use the iterative least square method to replace the ridge regression calculation of BLS, which avoids the selection of λ. Second, we provide the formulas of the driving amount and optimization solution under specific conditions. Third, the universal approximation property of DA-BLS is given. Last but not the least, extensive experimental results on the 1-D nonlinear function, UCI data-sets, and fault diagnosis of TEP show that DA-BLS outperforms the relevant methods such as BLS and the stochastic configuration network.

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