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Ying Cai

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6 papers

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EAAI Journal 2025 Journal Article

Medical artificial intelligence for early detection of lung cancer: A survey

Guohui Cai
Ying Cai
Zeyu Zhang
Yuanzhouhan Cao
Lin Wu
Daji Ergu
Zhibin Liao
Yang Zhao

Lung cancer remains one of the leading causes of morbidity and mortality worldwide, making early diagnosis critical for improving therapeutic outcomes and patient prognosis. Computer-aided diagnosis systems, which analyze computed tomography images, have proven effective in detecting and classifying pulmonary nodules, significantly enhancing the detection rate of early-stage lung cancer. Although traditional machine learning algorithms have been valuable, they exhibit limitations in handling complex sample data. The recent emergence of deep learning has revolutionized medical image analysis, driving substantial advancements in this field. This review focuses on recent progress in deep learning for pulmonary nodule detection, segmentation, and classification. Traditional machine learning methods, such as support vector machines and k-nearest neighbors, have shown limitations, paving the way for advanced approaches like Convolutional Neural Networks, Recurrent Neural Networks, and Generative Adversarial Networks. The integration of ensemble models and novel techniques is also discussed, emphasizing the latest developments in lung cancer diagnosis. Deep learning algorithms, combined with various analytical techniques, have markedly improved the accuracy and efficiency of pulmonary nodule analysis, surpassing traditional methods, particularly in nodule classification. Although challenges remain, continuous technological advancements are expected to further strengthen the role of deep learning in medical diagnostics, especially for early lung cancer detection and diagnosis. A comprehensive list of lung cancer detection models reviewed in this work is available at https: //github. com/CaiGuoHui123/Awesome-Lung-Cancer-Detection.

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AAAI Conference 2018 Conference Paper

Detecting Adversarial Examples Through Image Transformation

Shixin Tian
Guolei Yang
Ying Cai

Deep Neural Networks (DNNs) have demonstrated remarkable performance in a diverse range of applications. Along with the prevalence of deep learning, it has been revealed that DNNs are vulnerable to attacks. By deliberately crafting adversarial examples, an adversary can manipulate a DNN to generate incorrect outputs, which may lead catastrophic consequences in applications such as disease diagnosis and selfdriving cars. In this paper, we propose an effective method to detect adversarial examples in image classiﬁcation. Our key insight is that adversarial examples are usually sensitive to certain image transformation operations such as rotation and shifting. In contrast, a normal image is generally immune to such operations. We implement this idea of image transformation and evaluate its performance in oblivious attacks. Our experiments with two datasets show that our technique can detect nearly 99% of adversarial examples generated by the state-of-the-art algorithm. In addition to oblivious attacks, we consider the case of white-box attacks. We propose to introduce randomness in the process of image transformation, which can achieve a detection ratio of around 70%.

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IJCAI Conference 2018 Conference Paper

Spatio-Temporal Check-in Time Prediction with Recurrent Neural Network based Survival Analysis

Guolei Yang
Ying Cai
Chandan K Reddy

We introduce a novel check-in time prediction problem. The goal is to predict the time a user will check-in to a given location. We formulate check-in prediction as a survival analysis problem and propose a Recurrent-Censored Regression (RCR) model. We address the key challenge of check-in data scarcity, which is due to the uneven distribution of check-ins among users/locations. Our idea is to enrich the check-in data with potential visitors, i. e. , users who have not visited the location before but are likely to do so. RCR uses recurrent neural network to learn latent representations from historical check-ins of both actual and potential visitors, which is then incorporated with censored regression to make predictions. Experiments show RCR outperforms state-of-the-art event time prediction techniques on real-world datasets.

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YNIMG Journal 2017 Journal Article

Dissociated roles of the parietal and frontal cortices in the scope and control of attention during visual working memory

Siyao Li
Ying Cai
Jing Liu
Dawei Li
Zifang Feng
Chuansheng Chen
Gui Xue

Mounting evidence suggests that multiple mechanisms underlie working memory capacity. Using transcranial direct current stimulation (tDCS), the current study aimed to provide causal evidence for the neural dissociation of two mechanisms underlying visual working memory (WM) capacity, namely, the scope and control of attention. A change detection task with distractors was used, where a number of colored bars (i. e. , two red bars, four red bars, or two red plus two blue bars) were presented on both sides (Experiment 1) or the center (Experiment 2) of the screen for 100ms, and participants were instructed to remember the red bars and to ignore the blue bars (in both Experiments), as well as to ignore the stimuli on the un-cued side (Experiment 1 only). In both experiments, participants finished three sessions of the task after 15min of 1. 5mA anodal tDCS administered on the right prefrontal cortex (PFC), the right posterior parietal cortex (PPC), and the primary visual cortex (VC), respectively. The VC stimulation served as an active control condition. We found that compared to stimulation on the VC, stimulation on the right PPC specifically increased the visual WM capacity under the no-distractor condition (i. e. , 4 red bars), whereas stimulation on the right PFC specifically increased the visual WM capacity under the distractor condition (i. e. , 2 red bars plus 2 blue bars). These results suggest that the PPC and PFC are involved in the scope and control of attention, respectively. We further showed that compared to central presentation of the stimuli (Experiment 2), bilateral presentation of the stimuli (on both sides of the fixation in Experiment 1) led to an additional demand for attention control. Our results emphasize the dissociated roles of the frontal and parietal lobes in visual WM capacity, and provide a deeper understanding of the neural mechanisms of WM.

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YNIMG Journal 2016 Journal Article

Dissociated neural substrates underlying impulsive choice and impulsive action

Qiang Wang
Chunhui Chen
Ying Cai
Siyao Li
Xiao Zhao
Li Zheng
Hanqi Zhang
Jing Liu

There is a growing consensus that impulsivity is a multifaceted construct that comprises several components such as impulsive choice and impulsive action. Although impulsive choice and impulsive action have been shown to be the common characteristics of some impulsivity-related psychiatric disorders, surprisingly few studies have directly compared their neural correlates and addressed the question whether they involve common or distinct neural correlates. We addressed this important empirical gap using an individual differences approach that could characterize the functional relevance of neural networks in behaviors. A large sample (n=227) of college students was tested with the delay discounting and stop-signal tasks, and their performances were correlated with the neuroanatomical (gray matter volume, GMV) and functional (resting-state functional connectivity, RSFC) measures, using multivariate pattern analysis (MVPA) and 10-fold cross-validation. Behavioral results showed no significant correlation between impulsive choice measured by discounting rate (k) and impulsive action measured by stop signal reaction time (SSRT). The GMVs in the right frontal pole (FP) and left middle frontal gyrus (MFG) were predictive of k, but not SSRT. In contrast, the GMVs in the right inferior frontal gyrus (IFG), supplementary motor area (SMA), and anterior cingulate cortex (ACC) could predict individuals' SSRT, but not k. RSFC analysis using the FP and right IFG as seed regions revealed two distinct networks that correspond well to the “waiting” and “stopping” systems, respectively. Furthermore, the RSFC between the FP and ventromedial prefrontal cortex (VMPFC) was predictive of k, whereas the RSFC between the IFG and pre-SMA was predictive of SSRT. These results demonstrate clearly neural dissociations between impulsive choice and impulsive action, provide new insights into the nature of impulsivity, and have implications for impulsivity-related disorders.

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TCS Journal 2009 Journal Article

Binary sequences with optimal autocorrelation

Ying Cai
Cunsheng Ding

Sequences have important applications in ranging systems, spread spectrum communication systems, multi-terminal system identification, code-division multiple access communication systems, global positioning systems, software testing, circuit testing, computer simulation, and stream ciphers. Sequences and error-correcting codes are also closely related. In this paper, we give a well rounded treatment of binary sequences with optimal autocorrelation. We survey known ones and construct new ones.

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