An immune-inspired semi-supervised algorithm for breast cancer diagnosis

Peng, Lingxi; Chen, Wenbin; Zhou, Wubai; Li, Fufang; Yang, Jin; Zhang, Jiandong

doi:10.1016/j.cmpb.2016.07.020

Cited by 61 publications

(19 citation statements)

References 19 publications

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“…Early detection can be a big plus in treating ovary cancer. The earlier cancer is detected, the higher probability it may be cured [4]. Women have several persistent or frequently occurring non-specific symptoms ac-cording to a recent case-control studies.…”

Section: Introductionmentioning

confidence: 83%

Ovary Cancer Detection using Decision Tree Classifiers based on Historical Data of Ovary Cancer Patients

et al. 2017

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Abstract. This research implements decision tree classifiers and artificial neural network to predict whether the patient will live with ovary cancer or not. Dataset was obtained from Danish Cancer Register and contains five Input parameters. Dataset contains some missing values and a noticeable improvement in accuracy was detected after removing them. Three features of the original dataset were shown to be the most significant: Mobility of the cancer, Surface of the cancer, and the Consistency of the cancer. The addition of the other two features (Size of the cancer and age of the patient) did not improve the results significantly. It was noticed that the patients with a cystic, but fixed and even cancer have always died from the ovary cancer. In contrast, the patients with uneven, but fixed and solid cancer have always survived the cancer. It is recommended to include more information about either the cancer or the patient to increase the chance of predicting the output of such patients.

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Section: Introductionmentioning

confidence: 83%

Ovary Cancer Detection using Decision Tree Classifiers based on Historical Data of Ovary Cancer Patients

et al. 2017

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“…Literature [17] proposes an algorithm based on code word characteristics, which enables G.729A to carry out QIM steganography to detect effectively in a short time. Paper [18] discussed the application of speech-based information hiding in the area of medical scenario. Literature [19] carries out a comprehensive test for the frame-by-frame bitwise negation of G.729A speech and finds that the speech has hidden bits, and a method of information hiding combined with matrix coding is proposed.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Research on covert communication channel based on modulation of common compressed speech codec

Huang

et al. 2020

Neural Comput & Applic

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As is well known, multimedia has been widely used in VoIP and mobile communications. Research on how to establish covert communication channel over the above popular public applications has been flourishing in recent years. This paper tries to present a novel and effective method to construct a covert channel over common compressed speech stream by embedding sense information into it. In our method, after analysing the characteristic features of the excitation pulse positions of the ITU-T G.723.1 and G.729A speech codec, we design a novel and effective covert communication channel by finely modulating the codes of excitation pulse positions of the above two codecs in line with the secret information to be hidden. To improve the embedding capacity of the proposed method, we also use all the odd/even characteristics of pulse code positions to conduct information hiding. To test and verify the proposed approach, experiments are conducted on several different scenarios. Experimental results show that our methods and algorithms perform a higher degree of secrecy and sound information embedding efficacy compared with exiting similar methods.Neural Computing and Applications https://doi.org/10.1007/s00521-020-04882-y( 0123456789().,-volV) (0123456789().,-volV)

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“…Semi-supervised learning from data is one of the fundamental challenges in artificial intelligence, which considers the problem when only a subset of the observations has corresponding class labels [9]. This issue is of immense practical interest in a broad range of application scenarios, such as abnormal activity detection [39], neurological diagnosis [28], computer vision [10], and recommender Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored.…”

Section: Introductionmentioning

confidence: 99%

Adversarial Variational Embedding for Robust Semi-supervised Learning

Zhang

Yao

Yuan

2019

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery &Amp; Data Mining

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Semi-supervised learning is sought for leveraging the unlabelled data when labelled data is difficult or expensive to acquire. Deep generative models (e.g., Variational Autoencoder (VAE)) and semisupervised Generative Adversarial Networks (GANs) have recently shown promising performance in semi-supervised classification for the excellent discriminative representing ability. However, the latent code learned by the traditional VAE is not exclusive (repeatable) for a specific input sample, which prevents it from excellent classification performance. In particular, the learned latent representation depends on a non-exclusive component which is stochastically sampled from the prior distribution. Moreover, the semi-supervised GAN models generate data from pre-defined distribution (e.g., Gaussian noises) which is independent of the input data distribution and may obstruct the convergence and is difficult to control the distribution of the generated data. To address the aforementioned issues, we propose a novel Adversarial Variational Embedding (AVAE) framework for robust and effective semi-supervised learning to leverage both the advantage of GAN as a high quality generative model and VAE as a posterior distribution learner. The proposed approach first produces an exclusive latent code by the model which we call VAE++, and meanwhile, provides a meaningful prior distribution for the generator of GAN. The proposed approach is evaluated over four different real-world applications and we show that our method outperforms the state-of-the-art models, which confirms that the combination of VAE++ and GAN can provide significant improvements in semisupervised classification.

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An immune-inspired semi-supervised algorithm for breast cancer diagnosis

Cited by 61 publications

References 19 publications

Ovary Cancer Detection using Decision Tree Classifiers based on Historical Data of Ovary Cancer Patients

Ovary Cancer Detection using Decision Tree Classifiers based on Historical Data of Ovary Cancer Patients

Research on covert communication channel based on modulation of common compressed speech codec

Adversarial Variational Embedding for Robust Semi-supervised Learning

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