Improving performance of breast cancer risk prediction using a new CAD-based region segmentation scheme

Heidari, Morteza; Khuzani, Abolfazl Zargari; Danala, Gopichandh; Qiu, Yuchen

doi:10.1117/12.2293361

Cited by 11 publications

(7 citation statements)

References 17 publications

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“…Heidari et al. developed a AI-based prediction scheme to predict the risk of developing breast cancer in the short term (less than 2 years) based on features extracted from negative screening mammograms that had enhanced breast density tissue ( 70 ). The dataset used in this study included craniocaudal (CC) views of 570 negative screening mammograms that had a follow up screening exam within 2 years where 285 of these cases were then cancer positive as confirmed by tissue biopsy and 285 cases remained screening negative.…”

Section: Applications Of Ai-based Quantitative Image Analysis and Pre...mentioning

confidence: 99%

“…In addition to the measured breast density from mammograms, other types of medical images have been explored to develop new imaging markers or AI-based prediction models to predict breast cancer risk in individual women, particularly the short-term risk, which can help better stratify women into different breast cancer screening groups (Table 2). Heidari et al developed a AI-based prediction scheme to predict the risk of developing breast cancer in the short term (less than 2 years) based on features extracted from negative screening mammograms that had enhanced breast density tissue (70). The dataset used in this study included craniocaudal (CC) views of 570 negative screening mammograms that had a follow up screening exam within 2 years where 285 of these cases were then cancer positive as confirmed by tissue biopsy and 285 cases remained screening negative.…”

Section: Prediction Of Breast Cancer Riskmentioning

confidence: 99%

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Applying artificial intelligence technology to assist with breast cancer diagnosis and prognosis prediction

et al. 2022

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Breast cancer remains the most diagnosed cancer in women. Advances in medical imaging modalities and technologies have greatly aided in the early detection of breast cancer and the decline of patient mortality rates. However, reading and interpreting breast images remains difficult due to the high heterogeneity of breast tumors and fibro-glandular tissue, which results in lower cancer detection sensitivity and specificity and large inter-reader variability. In order to help overcome these clinical challenges, researchers have made great efforts to develop computer-aided detection and/or diagnosis (CAD) schemes of breast images to provide radiologists with decision-making support tools. Recent rapid advances in high throughput data analysis methods and artificial intelligence (AI) technologies, particularly radiomics and deep learning techniques, have led to an exponential increase in the development of new AI-based models of breast images that cover a broad range of application topics. In this review paper, we focus on reviewing recent advances in better understanding the association between radiomics features and tumor microenvironment and the progress in developing new AI-based quantitative image feature analysis models in three realms of breast cancer: predicting breast cancer risk, the likelihood of tumor malignancy, and tumor response to treatment. The outlook and three major challenges of applying new AI-based models of breast images to clinical practice are also discussed. Through this review we conclude that although developing new AI-based models of breast images has achieved significant progress and promising results, several obstacles to applying these new AI-based models to clinical practice remain. Therefore, more research effort is needed in future studies.

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Section: Applications Of Ai-based Quantitative Image Analysis and Pre...mentioning

confidence: 99%

Section: Prediction Of Breast Cancer Riskmentioning

confidence: 99%

Applying artificial intelligence technology to assist with breast cancer diagnosis and prognosis prediction

et al. 2022

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“…Nowadays, neural networks and deep learning models are important parts of detection, prediction, classification, segmentation, and recognition systems with different applications [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28]. U-Net is a convolutional neural network (CNN) architecture used for accurate and fast image segmentation [10].…”

Section: B U-net Structurementioning

confidence: 99%

Deep learning denoising for EOG artifacts removal from EEG signals

Mashhadi

Khuzani

Heidari

et al. 2020

2020 IEEE Global Humanitarian Technology Conference (GHTC)

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There are many sources of interference encountered in the electroencephalogram (EEG) recordings, specifically ocular, muscular, and cardiac artifacts. Rejection of EEG artifacts is an essential process in EEG analysis since such artifacts cause many problems in EEG signals analysis. One of the most challenging issues in EEG denoising processes is removing the ocular artifacts where Electrooculographic (EOG), and EEG signals have an overlap in both frequency and time domains. In this paper, we build and train a deep learning model to deal with this challenge and remove the ocular artifacts effectively. In the proposed scheme, we convert each EEG signal to an image to be fed to a U-NET model, which is a deep learning model usually used in image segmentation tasks. We proposed three different schemes and made our U-NET based models learn to purify contaminated EEG signals similar to the process used in the image segmentation process. The results confirm that one of our schemes can achieve a reliable and promising accuracy to reduce the Mean square error between the target signal (Pure EEGs) and the predicted signal (Purified EEGs).

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“…Nonlinear Intensity Transformation (NIT) algorithms as the second category of image enhancement methods enhance image contrast by mapping the intensity value of each pixel to another amount. These methods are simple and suitable for real-time applications [10,20].…”

Section: Related Workmentioning

confidence: 99%

Image quality enhancement in wireless capsule endoscopy with Adaptive Fraction Gamma Transformation and Unsharp Masking filter

Ezatian

Khaledyan

Jafari

et al. 2020

2020 IEEE Global Humanitarian Technology Conference (GHTC)

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Wireless Capsule Endoscopy (WCE) presented in 2001 as one of the key approaches to observe the entire gastrointestinal (GI) tract, generally the small bowels. It has been used to detect diseases in the gastrointestinal tract. Endoscopic image analysis is still a required field with many open problems. The quality of many images it produced is rather unacceptable due to the nature of this imaging system, which causes some issues to prognosticate by physicians and computer-aided diagnosis. In this paper, a novel technique is proposed to improve the quality of images captured by the WCE. More specifically, it enhanced the brightness, contrast, and preserve the color information while reducing its computational complexity. Furthermore, the experimental results of PSNR and SSIM confirm that the error rate in this method is near to the ground and negligible. Moreover, the proposed method improves intensity restricted average local entropy (IRMLE) by 22%, color enhancement factor (CEF) by 10%, and can keep the lightness of image effectively. The performances of our method have better visual quality and objective assessments in compare to the state-of-art methods.

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Improving performance of breast cancer risk prediction using a new CAD-based region segmentation scheme

Cited by 11 publications

References 17 publications

Applying artificial intelligence technology to assist with breast cancer diagnosis and prognosis prediction

Applying artificial intelligence technology to assist with breast cancer diagnosis and prognosis prediction

Deep learning denoising for EOG artifacts removal from EEG signals

Image quality enhancement in wireless capsule endoscopy with Adaptive Fraction Gamma Transformation and Unsharp Masking filter

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