Article
Medicine, General & Internal
Veronique Bouvard, Nicolas Wentzensen, Anne Mackie, Johannes Berkhof, Julia Brotherton, Paolo Giorgi-Rossi, Rachel Kupets, Robert Smith, Silvina Arrossi, Karima Bendahhou, Karen Canfell, Z. Mike Chirenje, Michael H. Chung, Marta del Pino, Silvia de Sanjose, Miriam Elfstrom, Eduardo L. Franco, Chisato Hamashima, Francoise F. Hamers, C. Simon Herrington, Raul Murillo, Suleeporn Sangrajrang, Rengaswamy Sankaranarayanan, Mona Saraiya, Mark Schiffman, Fanghui Zhao, Marc Arbyn, Walter Prendiville, Blanca I. Indave Ruiz, Isabel Mosquera-Metcalfe, Beatrice Lauby-Secretan
Summary: This article reviews the best methods of screening for cervical cancer, with HPV nucleic acid testing being superior whether used alone or in combination with other methods.
NEW ENGLAND JOURNAL OF MEDICINE
(2021)
Article
Multidisciplinary Sciences
Shenghua Cheng, Sibo Liu, Jingya Yu, Gong Rao, Yuwei Xiao, Wei Han, Wenjie Zhu, Xiaohua Lv, Ning Li, Jing Cai, Zehua Wang, Xi Feng, Fei Yang, Xiebo Geng, Jiabo Ma, Xu Li, Ziquan Wei, Xueying Zhang, Tingwei Quan, Shaoqun Zeng, Li Chen, Junbo Hu, Xiuli Liu
Summary: Computer-assisted diagnosis plays a crucial role in enhancing cervical cancer screening. The study introduces a deep learning-based WSI classification and lesion cell recommendation system, achieving comparable results with cytologists. By combining low- and high-resolution WSIs and utilizing a recurrent neural network model, the system successfully analyzes WSIs and evaluates lesion degree, demonstrating promising performance for clinical applications.
NATURE COMMUNICATIONS
(2021)
Article
Biochemistry & Molecular Biology
Kajsa Ericson Lindquist, Inga Gudinaviciene, Nektaria Mylona, Rodrigo Urdar, Maria Lianou, Eva Darai-Ramqvist, Felix Haglund, Matyas Bendek, Erika Bardoczi, Katalin Dobra, Hans Brunnstroem
Summary: This study evaluated the real-world accuracy and use of immunohistochemical staining in lung cancer diagnostics, finding high overall diagnostic accuracy of small specimens but also instances of discordant diagnosis. Improvements in routine practice, such as including specific markers in the IHC panel, could enhance diagnostic accuracy and efficiency.
Article
Oncology
Anna Macios, Katarzyna Komerska, Andrzej Nowakowski
Summary: The study analyzed the results of FN slides audit in the Polish Cervical Cancer Screening Program (CCSP) from 2010 to 2013 and identified risk factors for obtaining TN results before CC diagnosis. Misinterpretation was the main reason for FN cytology in the CCSP, indicating the need for further personnel training to improve screening quality. The low agreement between auditors also requires further investigation and a standardized process for auditor selection should be planned to improve audit quality.
Article
Oncology
Jon Frias-Gomez, Eva Tovar, August Vidal, Lluis Murgui, Raquel Ibanez, Paula Peremiquel-Trillas, Sonia Paytubi, Nuria Baixeras, Alba Zanca, Jordi Ponce, Marta Pineda, Joan Brunet, Silvia de Sanjose, Francesc Xavier Bosch, Xavier Matias-Guiu, Laia Alemany, Laura Costas
Summary: The study aimed to estimate the sensitivity of cervical cytology in detecting endometrial cancer, finding a low sensitivity in diagnosing endometrial cancer and suggesting the potential role of genomics and epigenomics in improving sensitivity.
Article
Obstetrics & Gynecology
Jacquelyn Dillon, Ling Chen, Alexander Melamed, Caryn M. St Clair, June Y. Hou, Fady Khoury-Collado, Allison Gockley, Melissa Accordino, Dawn L. Hershman, Jason D. Wright
Summary: Cervical cancer screening is frequently overused among average-risk Medicaid beneficiaries, with women who do not undergo screening also unlikely to receive routine gynecological examinations.
BJOG-AN INTERNATIONAL JOURNAL OF OBSTETRICS AND GYNAECOLOGY
(2022)
Article
Medicine, General & Internal
Yang Liu, Yongxiang Yin, Yi Zhang, Luling Lin, Min Zhao, Qi Chen
Summary: This study aimed to investigate the concordance between ThinPrep cytology and histology test in the diagnosis of cervical cancer and HSIL in HPV-positive women. The results showed false negative results for cervical cancer and HSIL in the liquid-based cytology test.
FRONTIERS IN MEDICINE
(2022)
Proceedings Paper
Engineering, Multidisciplinary
Apoorva Gupta, Ashutosh Anand, Yasha Hasija
Summary: This paper generalizes the approach of cervical cancer detection through four diagnostic tests - Hinselmann's test, Schiller's test, Biopsy, and Cytology, with a focus on achieving higher recall scores and reducing false positive values. The emphasis is on recall-based approach over accuracy and precision, utilizing techniques like confusion matrix and ROC for individual test analysis.
2021 6TH INTERNATIONAL CONFERENCE FOR CONVERGENCE IN TECHNOLOGY (I2CT)
(2021)
Article
Medicine, General & Internal
David Robert Grimes, Edward M. A. Corry, Talia Malagon, Ciaran O'Riain, Eduardo L. Franco, Donal J. Brennan
Summary: The study compared different cervical cancer screening modalities on a simulated population of women aged >= 25 years, considering the increasing uptake of the HPV vaccine. HPV-based modalities outperformed LBC-based approaches, with reflex approaches and appropriate test intervals maximizing therapeutic efficacy. Increasing HPV vaccination rates resulted in fewer unnecessary colposcopies with HPV-based screening approaches.
Review
Oncology
Felice Crocetto, Biagio Barone, Matteo Ferro, Gian Maria Busetto, Evelina La Civita, Carlo Buonerba, Giuseppe Di Lorenzo, Daniela Terracciano, Jack A. Schalken
Summary: Bladder cancer is the most common malignancy of the urinary tract. Traditional diagnostic methods are invasive and uncomfortable, while alternative methods have poor sensitivity. Therefore, an improved non-invasive approach is needed for the management of bladder cancer.
CRITICAL REVIEWS IN ONCOLOGY HEMATOLOGY
(2022)
Article
Biochemistry & Molecular Biology
Mohammed S. Mansour, Karina Malmros, Ulrich Mager, Kajsa Ericson Lindquist, Kim Hejny, Benjamin Holmgren, Tomas Seidal, Annika Dejmek, Katalin Dobra, Maria Planck, Hans Brunnstrom
Summary: This study aimed to explore the potential impacts of various clinicopathological and molecular factors on PD-L1 expression. The results showed that tumor histology and mutational status were correlated with PD-L1 expression. These findings are important for further understanding the treatment response in this disease.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2022)
Article
Multidisciplinary Sciences
Yuki Kurita, Shiori Meguro, Naoko Tsuyama, Isao Kosugi, Yasunori Enomoto, Hideya Kawasaki, Takashi Uemura, Michio Kimura, Toshihide Iwashita
Summary: Deep learning technology is used in the medical field to create devices for clinical practice. In cytology, deep learning methods can improve cancer screening by providing quantitative and objective testing. However, constructing accurate deep learning models requires a large amount of labeled data. To address this, we used the Noisy Student Training technique to develop a binary classification model for cervical cytology screening with reduced labeled data. The model achieved high accuracy and reliability in classifying normal and abnormal images, making it a promising tool for cervical cytology screening.
Article
Medicine, General & Internal
Mandy Man-Yee Chu, Charleen Sze-Yan Cheung, Siew-Fei Ngu, Ka-Yu Tse, Philip Pun-Ching Ip, Annie Nga-Yin Cheung, Hextan Yuen-Sheung Ngan, Karen Kar-Loen Chan
Summary: This study aimed to compare the diagnostic efficacy of colposcopic-directed biopsy and four-quadrant biopsy in detecting high-grade cervical intra-epithelial neoplasia (CIN). A total of 1311 women attending routine cervical screening were recruited, with 118 cases of high-grade CIN diagnosed. Four-quadrant biopsy detected significantly more high-grade CIN than colposcopic-directed biopsy, especially in patients with low-grade/normal/unsatisfactory colposcopy. Four-quadrant cervical biopsies should be considered for all women with an abnormal smear or positive HPV testing.
Article
Oncology
Jiangrong Wang, Henrik Edvardsson, Bjorn Strander, Bengt Andrae, Par Sparen, Joakim Dillner
Summary: The increase in cervical cancer incidence in Sweden from 2014 to 2015 was attributed to false-negative cytological findings. A long-term follow-up was performed to investigate if the problem persisted. The incidence rates of invasive cervical cancer were reported for different years, showing no overall change but a significant increase among women aged 50 to 60 with normal cytology. The study suggests the need for improved triaging and quality assurance.
INTERNATIONAL JOURNAL OF CANCER
(2023)
Article
Multidisciplinary Sciences
Karin Sundstrom, Helena Lamin, Joakim Dillner
Summary: The study evaluated the cobas 6800 HPV test system for large-scale organized cervical screening programs and found that it had similar, high performance to the cobas 4800 test, with high relative sensitivity and specificity.
Article
Engineering, Electrical & Electronic
Shobhit K. Patel, Sunil P. Lavadiya, Juveriya Parmar, Sudipta Das, Kawsar Ahmed, Sofyan A. Taya
Summary: This article presents an innovative and simple method for achieving broadband frequency reconfigurable antenna structure using low-cost materials and a compact design. The frequency reconfigurability is achieved through the OFF and ON mechanisms of three PIN diodes, and various performance observations are conducted to evaluate its effectiveness. The design proves to be suitable for different applications in the X frequency band, with a wide broadband frequency tunability of 2.5 GHz.
INTERNATIONAL JOURNAL OF MICROWAVE AND WIRELESS TECHNOLOGIES
(2023)
Article
Engineering, Electrical & Electronic
Krupal Jivrajani, Shobhit K. Patel, Chandrasinh Parmar, Jaymit Surve, Kawsar Ahmed, Francis M. Bui, Fahad Ahmed Al-Zahrani
Summary: In order to address the difficulties faced by visually impaired individuals in traveling and accessing accurate information, we propose a smart stick solution. By incorporating a camera, pulse sensor, and GPS on the stick, it helps in detecting obstacles, recognizing objects and currency, monitoring user health, providing emergency alerts, and offering bus timing information. This system is cost-effective, lightweight, and user-friendly.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Engineering, Biomedical
Shobhit K. Patel, Jaymit Surve, Juveriya Parmar, Kawsar Ahmed, Francis M. Bui, Fahad Ahmed Al-Zahrani
Summary: This century has seen the emergence of deadly and infectious diseases like influenza virus, Ebola virus, Zika virus, and the highly infectious SARS-CoV-2 (COVID-19), leading to epidemics and pandemics worldwide. Early detection of these viruses is crucial in saving lives, as some of them lack proper medications and vaccines. Although vaccines are available for COVID-19, new variants like Delta and Omicron are spreading rapidly. Existing virus detection techniques are time-consuming, expensive, and prone to false results. Biosensors offer a promising solution to this challenge, providing efficient and cost-effective early-stage illness detection. They have broad applications in healthcare, wearable electronics, safety, environment, military, and agriculture. This systematic review aims to summarize recent advancements in biosensor-based detection of pandemic viruses, including COVID-19, to assist fellow researchers in developing adaptable virus biosensors.
IEEE REVIEWS IN BIOMEDICAL ENGINEERING
(2023)
Article
Biology
Md Arju Hossain, Md Habibur Rahman, Habiba Sultana, Asif Ahsan, Saiful Islam Rayhan, Md Imran Hasan, Md Sohel, Pratul Dipta Somadder, Mohammad Ali Moni
Summary: The study aimed to investigate the pharmacological mechanism behind apigenin's role in the synergetic effects of COVID-19 to the progression of HIV patients. The research found that apigenin has strong interactions with certain key proteins and its pharmacokinetic features suggest it is an effective therapeutic agent. However, more in vitro and in vivo studies are needed to validate these findings.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Biology
Md. Ali Hossain, Tania Akter Asa, Md. Rabiul Auwul, Md. Aktaruzzaman, Md. Mahfizur Rahman, M. Zahidur Rahman, Mohammad Ali Moni
Summary: This study aimed to understand the influence of smoking on COVID-19 infected patients by analyzing the transcriptomics data of lung epithelial cells. The analysis revealed dysregulated genes and pathways associated with smoking and COVID-19 infection. The network analysis identified key proteins overlapping between smoking and COVID-19, and the gene ontology and pathways analysis suggested potential therapeutic targets for smoking individuals with COVID-19.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Biology
F. M. Javed Mehedi Shamrat, Sami Azam, Asif Karim, Kawsar Ahmed, Francis M. Bui, Friso De Boer
Summary: Multiple lung diseases are diagnosed using a Neural Network algorithm. A fine-tuned MobileLungNetV2 model achieves an extraordinary classification accuracy of 96.97% on pre-processed data from the ChestX-ray14 dataset. The model shows promising results in classifying multiple lesions on Chest X-ray images.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Nanoscience & Nanotechnology
M. B. Hossain, K. A. J. Alsalem, K. Ahmed, F. M. Bui, S. M. Ibrahim, S. K. Patel
Summary: A PCF sensor based on Zeonex fiber material is proposed for chemical sensing. The sensor exhibits high relative sensitivity for glycerol, acetic acid, and water at a frequency of 3.5 THz. It also has low effective material loss and confinement loss, making it an efficient chemical sensor. Moreover, the sensor can be easily fabricated using modern techniques.
DIGEST JOURNAL OF NANOMATERIALS AND BIOSTRUCTURES
(2023)
Article
Clinical Neurology
Anita Sathyanarayanan, Tamara T. Mueller, Mohammad Ali Moni, Katja Schueler, Bernhard T. Baune, Pietro Lio, Divya Mehta
Summary: This review summarizes the methods for discovering biologically meaningful biomarkers for diagnosis, treatment, and prognosis by combining multi-omics data. It discusses conventional and state-of-the-art statistical and machine learning approaches, as well as the role of biological model systems and in silico multi-omics model designs in clinical translation of psychiatric research. Challenges and future applications of multi-omics integration in psychiatric research are also discussed.
EUROPEAN NEUROPSYCHOPHARMACOLOGY
(2023)
Article
Chemistry, Analytical
Kawsar Ahmed, Francis M. Bui, Fang-Xiang Wu
Summary: To reduce the development time and effort of standard optical biosensors, machine learning approaches have been used to predict crucial parameters and evaluate the performance of the models based on performance indicators.
Article
Computer Science, Artificial Intelligence
Md. Alamin Talukder, Md. Manowarul Islam, Md. Ashraf Uddin, Arnisha Akhter, Md. Alamgir Jalil Pramanik, Sunil Aryal, Muhammad Ali Abdulllah Almoyad, Khondokar Fida Hasan, Mohammad Ali Moni
Summary: Brain tumors are fatal and devastating, reducing life expectancy significantly. Accurate diagnosis is crucial for treatment plans. Manual analysis of MRI data is challenging and time-consuming, calling for a reliable deep learning model.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Biochemical Research Methods
Shobhit K. Patel, Jaymit Surve, Juveriya Parmar, Tanvirjah Parmar, Rajendrasinh Jadeja, Kawsar Ahmed, Francis M. Bui
Summary: A numerically investigated graphene disk metasurface-inspired refractive index sensor with a subwavelength structure is proposed to enhance the functionality of flexible metasurface in the biosensor sector. The sensor aims to detect amino acids with high sensitivity. The optimal design is achieved by varying several structural parameters, and the sensor exhibits wide-angle insensitivity. The sensor's attributes, including maximum sensitivity, Figure of Merit (FOM), and Q-factor, are analyzed, offering insights for designing metasurface biosensors with high sensitivity in amino acid detection.
IEEE TRANSACTIONS ON NANOBIOSCIENCE
(2023)
Article
Engineering, Multidisciplinary
Shaymaa R. Tahhan, Arkadiy Mastin, Izaddeen Kabir Yakasai, Ahmad Atieh, Kawsar Ahmed, Francis M. Bui, Fahad Ahmed Al-Zahrani
Summary: This research proposes a photonic crystal fiber made of tellurite with unique optical guiding properties. Simulation results show that the fiber can achieve high nonlinearity and zero-dispersion at 1650 nm. It can generate a broad spectrum of supercontinuum and has potential applications in various fields.
ALEXANDRIA ENGINEERING JOURNAL
(2023)
Article
Biotechnology & Applied Microbiology
Fahad Ahmed Al-Zahrani, Lway Faisal Abdulrazak, Mamun Ali, Nazrul Islam, Kawsar Ahmed
Summary: This study aimed to build a machine learning-based predictive model to predict the level of depression among physicians and identify relevant risk factors. StackDPP classifier outperformed other classification algorithms on all sub-datasets. The proposed model showed high capability in predicting depression levels and identifying significant risk factors.
BIOENGINEERING-BASEL
(2023)
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)