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Yao Chen

Bio: Yao Chen is an academic researcher from Sichuan University. The author has contributed to research in topics: Artificial intelligence & Computer science. The author has an hindex of 3, co-authored 4 publications receiving 85 citations.

Papers
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Journal ArticleDOI
Xiaofeng Qi1, Lei Zhang1, Yao Chen1, Yong Pi1, Chen Yi1, Qing Lv1, Zhang Yi1 
TL;DR: An automated breast cancer diagnosis model for ultrasonography images using deep convolutional neural networks with multi‐scale kernels and skip connections is developed and achieves a performance comparable to human sonographers and can be applied to clinical scenarios.

148 citations

Journal ArticleDOI
Yong Pi1, Yao Chen1, Dan Deng, Xiaofeng Qi1, Jilan Li1, Qing Lv1, Zhang Yi1 
TL;DR: This paper formulate the diagnosis of breast cancer on ultrasonography images as a Multiple Instance Learning (MIL) problem, diagnosing a breast nodule by jointly analyzing the nodule on multiple planes and develops an attention-augmented deep neural network to solve this problem.

13 citations

Journal ArticleDOI
TL;DR: Wang et al. as mentioned in this paper developed an automated breast cancer diagnosis system based on stacked denoising autoencoders and generative adversarial networks, which is deployed on mobile phones, takes a photo of the ultrasound report as input and performs diagnosis on each image.

8 citations

Patent
29 Jun 2018
TL;DR: In this paper, the breast cancer detection method is applied to a server, which consists of the following steps of acquiring a detection report uploaded by a user through a network; determining whether the detection report contains color Doppler ultrasound images; acquiring a plurality of target color Doppersler ultrasound image carried in the detected report; and carrying out lesion detection and outputting a detection result used for expressing whether the user has a breast cancer.
Abstract: The embodiments of the invention provide a breast cancer detection method and a device and belong to the computer technology field. The breast cancer detection method is applied to a server. The method comprises the following steps of acquiring a detection report uploaded by a user through a network; determining whether the detection report contains color Doppler ultrasound images; if the detection report contains the color Doppler ultrasound images, acquiring a plurality of target color Doppler ultrasound images carried in the detection report; and based on the plurality of target color Doppler ultrasound images, carrying out lesion detection and outputting a detection result used for expressing whether the user has a breast cancer. Therefore, the user only needs to use a mobile phone totake photos of the breast color Doppler ultrasound image report issued by a hospital, and upload the color Doppler ultrasound image to the server so that identification can be performed. Whether a patient has the breast cancer or other mammary gland diseases is determined and a diagnosis opinion can be timely provided.

4 citations

Journal ArticleDOI
TL;DR: In this article , a double-layer (Levenberg-Marquardt) optimization method with outlier points screening is proposed to reduce the influence of random errors in robot vision systems and improve the calibration accuracy of the robot hand-eye matrix.
Abstract: In monocular vision robot systems, the hand-eye calibration approach is crucial for ensuring operational accuracy. A double-layer (Levenberg–Marquardt ([LM] method) optimization (DLMO) method with outlier points screening is proposed to reduce the influence of random errors in robot vision systems and improve the calibration accuracy of the robot hand-eye matrix. First, the equation of the hand-eye matrix is established, and the initial value of the hand-eye matrix is solved by the linear least square method. Second, the Euler angle transformation is applied to the rotation matrix part to ensure its orthogonality. Next, an optimization model of the hand-eye matrix is constructed, and the traditional LM optimization method is used to optimize the initial hand-eye matrix for the first time. Finally, the optimization model of the hand-eye matrix is modified, and the LM optimization method with the outlier sample points screening is applied to optimize the hand-eye matrix for the second time. Hand-eye calibration tests are conducted on an industrial robot equipped with a monocular vision system using the proposed method. Experimental results demonstrate that the average position error of the calibration results obtained by the proposed DLMO method is 0.22 mm, which is superior to the traditional hand-eye calibration method and meets the working requirements of the vision robot in the industrial field.

1 citations


Cited by
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Journal ArticleDOI
TL;DR: This study aims at presenting a review that shows the new applications of machine learning and deep learning technology for detecting and classifying breast cancer and provides an overview of progress and the future trends and challenges in the classification and detection of breast cancer.
Abstract: Breast cancer is the second leading cause of death for women, so accurate early detection can help decrease breast cancer mortality rates. Computer-aided detection allows radiologists to detect abnormalities efficiently. Medical images are sources of information relevant to the detection and diagnosis of various diseases and abnormalities. Several modalities allow radiologists to study the internal structure, and these modalities have been met with great interest in several types of research. In some medical fields, each of these modalities is of considerable significance. This study aims at presenting a review that shows the new applications of machine learning and deep learning technology for detecting and classifying breast cancer and provides an overview of progress in this area. This review reflects on the classification of breast cancer utilizing multi-modalities medical imaging. Details are also given on techniques developed to facilitate the classification of tumors, non-tumors, and dense masses in various medical imaging modalities. It first provides an overview of the different approaches to machine learning, then an overview of the different deep learning techniques and specific architectures for the detection and classification of breast cancer. We also provide a brief overview of the different image modalities to give a complete overview of the area. In the same context, this review was performed using a broad variety of research databases as a source of information for access to various field publications. Finally, this review summarizes the future trends and challenges in the classification and detection of breast cancer.

164 citations

Journal ArticleDOI
TL;DR: A novel method to segment the breast tumor via semantic classification and merging patches and achieved competitive results compared to conventional methods in terms of TP and FP, and produced good approximations to the hand-labelled tumor contours.

135 citations

Journal ArticleDOI
TL;DR: A selective kernel (SK) U-Net convolutional neural network to adjust network’s receptive fields via an attention mechanism, and fuse feature maps extracted with dilated and conventional convolutions for breast mass segmentation in ultrasound (US).

99 citations

Journal ArticleDOI
TL;DR: An overview of explainable artificial intelligence (XAI) used in deep learning-based medical image analysis can be found in this article , where a framework of XAI criteria is introduced to classify deep learning based medical image classification methods.

94 citations

Posted Content
TL;DR: An overview of eXplainable Artificial Intelligence (XAI) used in deep learning-based medical image analysis is presented in this paper, where a framework of XAI criteria is introduced to classify deep learning based methods.
Abstract: With an increase in deep learning-based methods, the call for explainability of such methods grows, especially in high-stakes decision making areas such as medical image analysis. This survey presents an overview of eXplainable Artificial Intelligence (XAI) used in deep learning-based medical image analysis. A framework of XAI criteria is introduced to classify deep learning-based medical image analysis methods. Papers on XAI techniques in medical image analysis are then surveyed and categorized according to the framework and according to anatomical location. The paper concludes with an outlook of future opportunities for XAI in medical image analysis.

92 citations