Article
Radiology, Nuclear Medicine & Medical Imaging
Lu Chen, Hui-yu Duan, Xiao-min Tang, Cheng-cheng Ma, Li Yang, Zong-yu Xie, Zhi-zhen Gao, Jian-fang Chen
Summary: The aim of this study was to establish a predictive nomogram for malignancy risk stratification of micro-calcifications detected on mammography. The results showed that the morphology, distribution, and maximum diameter of the micro-calcifications, as well as menopausal status, were independent predictors of malignant micro-calcifications. Based on these factors, a nomogram was developed and demonstrated reliable discrimination performance.
ACADEMIC RADIOLOGY
(2022)
Article
Radiology, Nuclear Medicine & Medical Imaging
Nazanin Mobini, Marina Codari, Francesca Riva, Maria Giovanna Ienco, Davide Capra, Andrea Cozzi, Serena Carriero, Diana Spinelli, Rubina Manuela Trimboli, Giuseppe Baselli, Francesco Sardanelli
Summary: This study implemented a deep convolutional neural network for automatic detection and quantification of breast arterial calcifications (BAC), which showed promising performances and a strong correlation with manual measurements.
EUROPEAN RADIOLOGY
(2023)
Article
Radiology, Nuclear Medicine & Medical Imaging
Huanhuan Liu, Yanhong Chen, Yuzhen Zhang, Lijun Wang, Ran Luo, Haoting Wu, Chenqing Wu, Huiling Zhang, Weixiong Tan, Hongkun Yin, Dengbin Wang
Summary: The combined deep learning model based on full-field digital mammography improves the prediction of malignancy in BI-RADS 4 microcalcifications, assisting junior radiologists to achieve better performance and optimize clinical decision-making.
EUROPEAN RADIOLOGY
(2021)
Article
Computer Science, Interdisciplinary Applications
Lazaros Tsochatzidis, Panagiota Koutla, Lena Costaridou, Ioannis Pratikakis
Summary: This study proposed a method to integrate segmentation information of mammographic lesions into convolutional neural networks for improved breast cancer diagnosis. Experimental results demonstrated that the proposed method achieved better performance in diagnosis.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2021)
Article
Radiology, Nuclear Medicine & Medical Imaging
Ann L. Brown, Rifat A. Wahab, Bin Zhang, Dana H. Smetherman, Mary C. Mahoney
Summary: This study aimed to evaluate the reporting practices of breast arterial calcification (BAC) and the perceptions of radiologists regarding BAC communication and follow-up recommendations. The survey results revealed variations in reporting practices and recommendations among radiologists, with older and more experienced radiologists more likely to include and value BAC in their breast imaging practice.
ACADEMIC RADIOLOGY
(2022)
Article
Multidisciplinary Sciences
Tariq Mahmood, Jianqiang Li, Yan Pei, Faheem Akhtar, Mujeeb Ur Rehman, Shahbaz Hassan Wasti
Summary: Breast cancer is a highly fatal illness among women globally. This study proposes a deep learning-based convolutional neural network model that reduces human error in diagnosing malignant breast tumors. Experimental results show that the model achieves high accuracy and sensitivity in classifying breast masses on mammograms, providing valuable help in fast computation of mammography and diagnosis and treatment planning of breast tumors.
Editorial Material
Biochemistry & Molecular Biology
Nehmat Houssami, Karla Kerlikowske
Summary: AI has the potential to be a new tool in the risk assessment and screening of breast cancer, but its impact on relevant clinical outcomes needs to be prospectively evaluated.
Article
Radiology, Nuclear Medicine & Medical Imaging
Yoel Shoshan, Ran Bakalo, Flora Gilboa-Solomon, Vadim Ratner, Ella Barkan, Michal Ozery-Flato, Mika Amit, Daniel Khapun, Emily B. Ambinder, Eniola T. Oluyemi, Babita Panigrahi, Philip A. DiCarlo, Michal Rosen-Zvi, Lisa A. Mullen
Summary: The study evaluated the use of artificial intelligence (AI) in improving the efficiency of digital breast tomosynthesis (DBT) screening. It found that AI can reduce the workload of radiologists without compromising sensitivity and recall rate compared to expert interpretations.
Article
Radiology, Nuclear Medicine & Medical Imaging
Alistair Mackenzie, Emma L. Thomson, Melissa Mitchell, Premkumar Elangovan, Chantal van Ongeval, Lesley Cockmartin, Lucy M. Warren, Louise S. Wilkinson, Matthew G. Wallis, Rosalind M. Given-Wilson, David R. Dance, Kenneth C. Young
Summary: For calcification clusters, there were no significant differences in FoM or LDF. For masses, the FoM and LDF were significantly improved in the arms using DBT compared to DM alone. On average, both calcification clusters and masses were more visible on DBT than on DM and SM images.
EUROPEAN RADIOLOGY
(2022)
Article
Public, Environmental & Occupational Health
Guy H. Montgomery, Julie B. Schnur, Joel Erblich, Jagat Narula, Kelley Benck, Laurie Margolies
Summary: The prevalence of BAC varies according to age, ethnicity, race, women's health, and breast-specific factors. Age is a significant risk factor, with BAC odds doubling approximately every decade. The study found higher BAC prevalence among Hispanic and Black women, lower prevalence among Ashkenazi women, nulliparous and pre-menopausal women, those with dense breasts and breast implants, and those currently using HRT. There was no association between BAC prevalence and BMI or age at menarche.
ANNALS OF EPIDEMIOLOGY
(2022)
Review
Oncology
Ioannis Sechopoulos, Jonas Teuwen, Ritse Mann
Summary: Screening for breast cancer has evolved rapidly over the past 30 years, with the introduction of digital technology and artificial intelligence becoming mainstream in breast cancer detection. Studies have shown that artificial intelligence performs on par with experienced radiologists in breast cancer screening.
SEMINARS IN CANCER BIOLOGY
(2021)
Article
Radiology, Nuclear Medicine & Medical Imaging
Ann L. Brown, Charmi Vijapura, Mitva Patel, Alexis De La Cruz, Rifat Wahab
Summary: Dense breast tissue is a strong independent risk factor for breast cancer, with a higher risk compared to fatty breasts. ABUS and MRI are effective in detecting breast cancer, and MRI is especially good at detecting DCIS. Awareness of breast density varies among different ethnic groups, with lower awareness among Asian, Hispanic, Black, and Jewish women compared to White women. Black and Hispanic women are less likely to receive supplemental screening.
Article
Multidisciplinary Sciences
Desiree Schliemann, Wilfred Mok Kok Hoe, Devi Mohan, Pascale Allotey, Daniel D. Reidpath, Min Min Tan, Nur Aishah Mohd Taib, Michael Donnelly, Tin Tin Su
Summary: This study investigated the challenges and opportunities of breast cancer screening and early detection in a low-income semi-rural community in Malaysia. The results highlighted barriers and opportunities in personal experiences, primary care, secondary care, community activities, and the link between public healthcare personnel and the community.
Article
Oncology
Debbie Lee Bennett, Andrea Marie Winter, Laura Billadello, Mary Catherine Lowdermilk, Christina Michelle Doherty, Sakina Kazmi, Sydney Laster, Noor Al-Hammadi, Anna Hardy, Daniel B. Kopans, Linda Moy
Summary: This study aimed to determine the feasibility of collecting method of detection (MOD) in a multicenter community registry and to compare outcomes and characteristics of breast cancer based on MOD in the United States. The results showed significant differences in outcome and characteristics of breast cancers based on MOD, and suggested that routine inclusion of MOD in US tumor registries would help quantify the impact of opportunistic screening mammography.
BREAST CANCER RESEARCH AND TREATMENT
(2023)
Review
Oncology
Nehmat Houssami, Sophia Zackrisson, Katrina Blazek, Kylie Hunter, Daniela Bernardi, Kristina Lang, Solveig Hofvind
Summary: Digital breast tomosynthesis (DBT) has been shown to significantly increase cancer detection rate (CDR) compared with mammography in breast cancer screening, but there is little difference in interval cancer rate (ICR) between DBT and mammography.
EUROPEAN JOURNAL OF CANCER
(2021)
Article
Computer Science, Artificial Intelligence
Long-Hao Yang, Jun Liu, Ying-Ming Wang, Hui Wang, Luis Martinez
Summary: The study focuses on enhancing the performance of rule-based systems on multi-class and multi-attribute problems through decomposition strategies and overlap functions. Experimental results demonstrate that the square product overlap function and the OVO strategy significantly improve the performance of EBRBS.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2022)
Article
Computer Science, Information Systems
Jiaojiao Niu, Degang Chen, Jinhai Li, Hui Wang
Summary: This study evaluates the classification performance of granular rules and proposes a new dynamic rule-based classification model (DRCM) based on updating mechanisms. The experiments show that updating granular reducts improves classification ability and DRCM achieves better performance on some datasets.
INFORMATION SCIENCES
(2022)
Article
Construction & Building Technology
Weiran Song, Shangyong Zhao, Yiming Zhang, Cheng Ruan, Ao Huang, Xiao Hu, Min Zhao, Wen Zhou, Ji Wang, Xuebao Wang, Hui Wang, Zongyu Hou, Zhe Wang
Summary: This study introduces the application of LIBS and chemometrics for rapid post-fire analysis of fire-retardant/resistant coatings. The method successfully identifies the types of FRC, heating devices, and predicts the heating temperatures with high accuracy. The results indicate that LIBS is a promising candidate for rapid on-site FRC analysis in fire protection and investigation.
CONSTRUCTION AND BUILDING MATERIALS
(2022)
Article
Physics, Applied
Tahereh Shah Mansouri, Hui Wang, Davide Mariotti, Paul Maguire
Summary: This study used optical emission spectroscopy and partial least squares-discriminant analysis to detect trace concentrations of methane gas. By enhancing the algorithms, the dimensionality and collinearity of the spectral emission data were addressed, leading to improved predictive performance.
JOURNAL OF PHYSICS D-APPLIED PHYSICS
(2022)
Article
Computer Science, Artificial Intelligence
Weiran Song, Muhammad Sher Afgan, Yong-Huan Yun, Hui Wang, Jiacheng Cui, Weilun Gu, Zongyu Hou, Zhe Wang
Summary: Laser-induced breakdown spectroscopy (LIBS) is a promising technique for multi-elemental analysis, but its data quality can be low due to matrix effects and signal uncertainty. Previous studies have attempted to improve LIBS performance using linear and nonlinear models, but they have limitations. In this work, a new machine learning algorithm called spectral knowledge-based regression (SKR) is proposed, which integrates linear and nonlinear models to improve the accuracy and interpretability of LIBS quantitative analysis.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Jiaojiao Niu, Degang Chen, Jinhai Li, Hui Wang
Summary: This article focuses on the learning of granular rules and proposes a novel fuzzy rule-based classification model. It improves the readability and efficiency through granular reducts and demonstrates its effectiveness with numerical experiments.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Chemistry, Analytical
Yiming Zhang, Weiran Song, Shangyong Zhao, Wen Zhou, Cheng Ruan, Hui Wang, Zhe Wang, Ji Wang, Xuebao Wang, Min Zhao
Summary: In this study, transparent intumescent fire retardant coatings (IFRC) brands were successfully identified using Raman spectroscopy and machine learning. Through pre-processing and classification algorithms, rapid, on-site, and low-cost identification can be achieved.
VIBRATIONAL SPECTROSCOPY
(2022)
Article
Computer Science, Artificial Intelligence
Pramod Gaur, Anirban Chowdhury, Karl McCreadie, Ram Bilas Pachori, Hui Wang
Summary: This study proposes a novel method using logistic regression with tangent space-based transfer learning for motor imagery-based BCI classification problems. The method improves classification accuracy by transforming EEG signal features and applying a logistic regression model. The performance of the method is tested on healthy subjects' data set and stroke patients' data set, and it outperforms existing methods in both cases.
IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS
(2022)
Article
Food Science & Technology
Ana M. Jimenez-Carvelo, Pengfei Li, Sara W. Erasmus, Hui Wang, Saskia M. van Ruth
Summary: One of the key components of food traceability systems is the unique identification and recording of products and batches along the supply chain, which can provide valuable information on emerging food frauds. The scanning of codes on food packaging by users generates spatial-temporal datasets that, when analyzed using artificial intelligence, can enhance current food fraud detection approaches. Spatial-temporal patterns of scanned codes can uncover emerging anomalies in supply chains caused by food fraud, and similar approaches from other fields have been discussed as potential transferable solutions for early warning of emerging food frauds in this paper.
Article
Biology
Jinfeng Wang, Shuaihui Huang, Zhiwen Wang, Dong Huang, Jing Qin, Hui Wang, Wenzhong Wang, Yong Liang
Summary: Alzheimer's disease (AD) is a mainstream senile disease worldwide. Predicting the early stage of AD is a key problem. This paper proposes an improved smooth classification framework combining wSGL112 as feature selection method and cSVM as classifier. The experimental results are supportive and satisfactory.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Computer Science, Artificial Intelligence
Sunanda Das, Md. Samir Imtiaz, Nieb Hasan Neom, Nazmul Siddique, Hui Wang
Summary: Sign language serves as a comprehensive medium of communication for individuals with hearing and speaking impairments. This paper presents a hybrid model and a background elimination algorithm for the automatic recognition of Bangla Sign Language. The proposed system achieves high accuracy and precision in character and digit recognition.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Engineering, Electrical & Electronic
Weiran Song, Hui Wang, Ultan F. Power, Enayetur Rahman, Judit Barabas, Jiandong Huang, James McLaughlin, Chris Nugent, Paul Maguire
Summary: This research investigates the potential of using low-cost portable near-infrared (NIR) spectroscopy and chemometrics to distinguish respiratory viruses. The results demonstrate the feasibility of this method for rapid, on-site, and low-cost virus prescreening for RSV and SeV, with the possibility of extending it to other viruses like SARS-CoV-2.
IEEE SENSORS JOURNAL
(2023)
Article
Food Science & Technology
Fayas Asharindavida, Omar Nibouche, James Uhomoibhi, Jun Liu, Jordan Vincent, Hui Wang
Summary: Machine learning is widely used for analyzing spectral data in food quality management. However, collecting high-quality spectral data from miniature spectrometers outside the laboratory is challenging. This study evaluates the data quality and characteristics of food datasets collected from miniature spectrometers, focusing on differentiating pure and adulterated olive oil using various machine learning models.
JOURNAL OF CONSUMER PROTECTION AND FOOD SAFETY
(2023)
Article
Computer Science, Artificial Intelligence
Huan Wan, Hui Wang, Bryan W. Scotney, Jun Liu, Xin Wei
Summary: The paper proposes a novel variant of subclass-based linear discriminant analysis (LDA) called Global Subclass Discriminant Analysis (GSDA) to address the limitation of traditional LDA in utilizing the locality information in data. GSDA selects subclasses from global clusters that may cross class boundaries, effectively utilizing both within-class and between-class information. Experimental results show that GSDA outperforms state-of-the-art LDA algorithms in terms of accuracy and run times.
KNOWLEDGE-BASED SYSTEMS
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Abbas Haider, Glenn Hawe, Hui Wang, Bryan Scotney
Summary: Market making is a trading activity that aims to profit from the bid-ask spread. Reinforcement learning has become popular for automated market making, but current methods are limited to single-asset modeling. Therefore, we propose a multi-task deep reinforcement learning model for multi-asset market making, achieving better investment returns.
MACHINE LEARNING, OPTIMIZATION, AND DATA SCIENCE (LOD 2021), PT II
(2022)
Article
Biology
Seyyed Bahram Borgheai, Alyssa Hillary Zisk, John McLinden, James Mcintyre, Reza Sadjadi, Yalda Shahriari
Summary: This study proposed a novel personalized scheme using fNIRS and EEG as the main tools to predict and compensate for the variability in BCI systems, especially for individuals with severe motor deficits. By establishing predictive models, it was found that there were significant associations between the predicted performances and the actual performances.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Hongliang Guo, Hanbo Liu, Ahong Zhu, Mingyang Li, Helong Yu, Yun Zhu, Xiaoxiao Chen, Yujia Xu, Lianxing Gao, Qiongying Zhang, Yangping Shentu
Summary: In this paper, a BDSMA-based image segmentation method is proposed, which improves the limitations of the original algorithm by combining SMA with DE and introducing a cooperative mixing model. The experimental results demonstrate the superiority of this method in terms of convergence speed and precision compared to other methods, and its successful application to brain tumor medical images.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Jingfei Hu, Linwei Qiu, Hua Wang, Jicong Zhang
Summary: This study proposes a novel semi-supervised point consistency network (SPC-Net) for retinal artery/vein (A/V) classification, addressing the challenges of specific tubular structures and limited well-labeled data in CNN-based approaches. The SPC-Net combines an AVC module and an MPC module, and introduces point set representations and consistency regularization to improve the accuracy of A/V classification.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Omair Ali, Muhammad Saif-ur-Rehman, Tobias Glasmachers, Ioannis Iossifidis, Christian Klaes
Summary: This study introduces a novel hybrid model called ConTraNet, which combines the strengths of CNN and Transformer neural networks, and achieves significant improvement in classification performance with limited training data.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Juan Antonio Valera-Calero, Dario Lopez-Zanoni, Sandra Sanchez-Jorge, Cesar Fernandez-de-las-Penas, Marcos Jose Navarro-Santana, Sofia Olivia Calvo-Moreno, Gustavo Plaza-Manzano
Summary: This study developed an easy-to-use application for assessing the diagnostic accuracy of digital pain drawings (PDs) compared to the classic paper-and-pencil method. The results demonstrated that digital PDs have higher reliability and accuracy compared to paper-and-pencil PDs, and there were no significant differences in assessing pain extent between the two methods. The PAIN EXTENT app showed good convergent validity.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Biao Qu, Jialue Zhang, Taishan Kang, Jianzhong Lin, Meijin Lin, Huajun She, Qingxia Wu, Meiyun Wang, Gaofeng Zheng
Summary: This study proposes a deep unrolled neural network, pFISTA-DR, for radial MRI image reconstruction, which successfully preserves image details using a preprocessing module, learnable convolution filters, and adaptive threshold.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Alireza Rafiei, Milad Ghiasi Rad, Andrea Sikora, Rishikesan Kamaleswaran
Summary: This study aimed to improve machine learning model prediction of fluid overload by integrating synthetic data, which could be translated to other clinical outcomes.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Jinlian Ma, Dexing Kong, Fa Wu, Lingyun Bao, Jing Yuan, Yusheng Liu
Summary: In this study, a new method based on MDenseNet is proposed for automatic segmentation of nodular lesions from ultrasound images. Experimental results demonstrate that the proposed method can accurately extract multiple nodules from thyroid and breast ultrasound images, with good accuracy and reproducibility, and it shows great potential in other clinical segmentation tasks.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Jiabao Sheng, SaiKit Lam, Jiang Zhang, Yuanpeng Zhang, Jing Cai
Summary: Omics fusion is an important preprocessing approach in medical image processing that assists in various studies. This study aims to develop a fusion methodology for predicting distant metastasis in nasopharyngeal carcinoma by mitigating the disparities in omics data and utilizing a label-softening technique and a multi-kernel-based neural network.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Zhenxiang Xiao, Liang He, Boyu Zhao, Mingxin Jiang, Wei Mao, Yuzhong Chen, Tuo Zhang, Xintao Hu, Tianming Liu, Xi Jiang
Summary: This study systematically investigates the functional connectivity characteristics between gyri and sulci in the human brain under naturalistic stimulus, and identifies unique features in these connections. This research provides novel insights into the functional brain mechanism under naturalistic stimulus and lays a solid foundation for accurately mapping the brain anatomy-function relationship.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Qianqian Wang, Mingyu Zhang, Aohan Li, Xiaojun Yao, Yingqing Chen
Summary: The development of PARP-1 inhibitors is crucial for the treatment of various cancers. This study investigates the structural regulation of PARP-1 by different allosteric inhibitors, revealing the basis of allosteric inhibition and providing guidance for the discovery of more innovative PARP-1 inhibitors.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Qing Xu, Wenting Duan
Summary: In this paper, a dual attention supervised module, named DualAttNet, is proposed for multi-label lesion detection in chest radiographs. By efficiently fusing global and local lesion classification information, the module is able to recognize targets with different sizes. Experimental results show that DualAttNet outperforms baselines in terms of mAP and AP50 with different detection architectures.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Kaja Gutowska, Piotr Formanowicz
Summary: The primary aim of this research is to propose algorithms for identifying significant reactions and subprocesses within biological system models constructed using classical Petri nets. These solutions enable two analysis methods: importance analysis for identifying critical individual reactions to the model's functionality and occurrence analysis for finding essential subprocesses. The utility of these methods has been demonstrated through analyses of an example model related to the DNA damage response mechanism. It should be noted that these proposed analyses can be applied to any biological phenomenon represented using the Petri net formalism. The presented analysis methods extend classical Petri net-based analyses, enhancing our comprehension of the investigated biological phenomena and aiding in the identification of potential molecular targets for drugs.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Hansle Gwon, Imjin Ahn, Yunha Kim, Hee Jun Kang, Hyeram Seo, Heejung Choi, Ha Na Cho, Minkyoung Kim, Jiye Han, Gaeun Kee, Seohyun Park, Kye Hwa Lee, Tae Joon Jun, Young-Hak Kim
Summary: Electronic medical records have potential in advancing healthcare technologies, but privacy issues hinder their full utilization. Deep learning-based generative models can mitigate this problem by creating synthetic data similar to real patient data. However, the risk of data leakage due to malicious attacks poses a challenge to traditional generative models. To address this, we propose a method that employs local differential privacy (LDP) to protect the model from attacks and preserve the privacy of training data, while generating medical data with reasonable performance.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Siwei Tao, Zonghan Tian, Ling Bai, Yueshu Xu, Cuifang Kuang, Xu Liu
Summary: This study proposes a transfer learning-based method to address the phase retrieval problem in grating-based X-ray phase contrast imaging. By generating a training dataset and using deep learning techniques, this method improves image quality and can be applied to X-ray 2D and 3D imaging.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)