Article
Computer Science, Artificial Intelligence
Xiaofeng Qi, Fasheng Yi, Lei Zhang, Yao Chen, Yong Pi, Yuanyuan Chen, Jixiang Guo, Jianyong Wang, Quan Guo, Jilan Li, Yi Chen, Qing Lv, Zhang Yi
Summary: Ultrasonography of the breast mass is an important imaging technology for diagnosing breast cancer, and ultrasound equipment is widely used in medical institutions in China. This study develops an automated breast cancer diagnosis system deployed on mobile phones, which improves diagnostic accuracy and aids in the early screening and diagnosis of breast cancer, reducing morbidity and mortality.
Article
Chemistry, Multidisciplinary
Jakub Kluk, Marek R. R. Ogiela
Summary: Advanced diagnosis systems can provide doctors with high-quality data for diagnosing diseases, like brain cancers, but humans may overlook tumor symptoms due to information overload. Therefore, the combination of diagnostic devices and software systems is becoming more common. This study focuses on designing a neural network system that can automatically diagnose brain tumors from MRI images and identify important areas. The research compared Convolutional Neural Networks and Vision Transformers, finding that both architectures achieved a high tumor recognition rate, but Vision Transformers were easier to train and provided more detailed decision reasoning. The results suggest that computer-aided diagnosis and Vision Transformers could play a significant role in the development of modern medicine in IoT and healthcare systems.
APPLIED SCIENCES-BASEL
(2022)
Article
Multidisciplinary Sciences
Yukihiro Nomura, Masato Hoshiyama, Shinsuke Akita, Hiroki Naganishi, Satoki Zenbutsu, Ayumu Matsuoka, Takashi Ohnishi, Hideaki Haneishi, Nobuyuki Mitsukawa
Summary: This study developed a computer-aided diagnosis software using deep learning for screening lower extremity lymphedema (LEL) in pelvic CT images. By using fat-enhanced images and the ResNet-34 model, high screening accuracy was achieved.
SCIENTIFIC REPORTS
(2023)
Article
Computer Science, Information Systems
Revathy Sivanandan, J. Jayakumari
Summary: Convolutional neural networks (CNNs) have been widely used for feature extraction and diagnosis in breast tumor images. To overcome the drawbacks of using pre-trained architectures, a novel CNN with optimized hyperparameters and a neutrosophic augmentation method was proposed. The optimized CNN achieved higher classification accuracy than other deep architectures and better segmentation metrics compared to U-Net and fully Convolutional Network based segmentation.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Biochemistry & Molecular Biology
Arthur Cartel Foahom Gouabou, Jules Collenne, Jilliana Monnier, Rabah Iguernaissi, Jean-Luc Damoiseaux, Abdellatif Moudafi, Djamal Merad
Summary: This study presents a new framework for automated melanoma diagnosis, which aims to improve the performance of existing systems and provide more transparency in the decision-making process by introducing the concept of players and decision theory. The proposed framework achieves good results in the diagnosis of melanoma, nevus, and benign keratosis, outperforming existing methods in this task. This approach could aid dermatologists in diagnosing challenging pigmented lesions and serve as a teaching tool for less experienced doctors.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2022)
Article
Engineering, Electrical & Electronic
Lamiaa Abdel-Hamid
Summary: A novel two-branched deep convolutional (TWEEC) network is developed for computer-aided glaucoma diagnosis, achieving high accuracies by extracting anatomical information related to the optic disc and surrounding blood vessels. Experimental results show that the network outperforms other deep networks in spatial and wavelet inputs, with the advantage of reducing overall training time by considering specific wavelet subbands.
INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY
(2022)
Article
Engineering, Biomedical
R. Karthik, R. Menaka, G. S. Kathiresan, M. Anirudh, M. Nagharjun
Summary: Breast cancer and breast tumors are the most common forms of cancer in medical practice, and early detection is crucial for saving lives. Recent advancements in radiological imaging and artificial intelligence techniques have improved the accuracy and efficiency of breast tumor classification. This research proposes a novel configuration of a Stacking Ensemble with custom Convolutional Neural Network architectures for accurate classification of breast tumors from ultrasound images.
Article
Radiology, Nuclear Medicine & Medical Imaging
Huiling Xiang, Yao-Sian Huang, Chu-Hsuan Lee, Ting-Yin Chang Chien, Cheng-Kuang Lee, Lixian Liu, Anhua Li, Xi Lin, Ruey-Feng Chang
Summary: The proposed 3-D tumor CADx system utilizing U-net and Res-CapsNet models showed promising results in accurately diagnosing tumors, especially non-mass lesions. The system outperformed other models and junior readers in terms of accuracy, sensitivity, and AUC values.
EUROPEAN JOURNAL OF RADIOLOGY
(2021)
Article
Computer Science, Artificial Intelligence
Sachin Mehta, Ximing Lu, Wenjun Wu, Donald Weaver, Hannaneh Hajishirzi, Joann G. Elmore, Linda G. Shapiro
Summary: In this study, a transformer-based holistic attention network (HATNet) is introduced for developing a computer-aided diagnosis method for classifying breast biopsy images. HATNet is capable of learning representations from gigapixel size images end-to-end, outperforming existing methods and matching the classification accuracy of human pathologists.
MEDICAL IMAGE ANALYSIS
(2022)
Article
Computer Science, Interdisciplinary Applications
Alessandro Sebastian Podda, Riccardo Balia, Silvio Barra, Salvatore Carta, Gianni Fenu, Leonardo Piano
Summary: Breast cancer is the most prevalent type of cancer among women worldwide. Early detection plays a crucial role in improving treatment outcomes and reducing complications and deaths. Ultrasound imaging is a commonly used technique for breast cancer diagnosis due to its low invasiveness and cost. However, its uncertainty has led to the development of computer-aided solutions. This study aims to design a fully-automated pipeline for segmenting and classifying breast lesions associated with cancer risk from ultrasound images. The proposed method employs convolutional neural networks and ensemble techniques, with an iterative optimization step. Experimental results show the superiority of the ensemble methods over individual networks, and the proposed method achieves competitive performance compared to the state-of-the-art.
JOURNAL OF COMPUTATIONAL SCIENCE
(2022)
Article
Oncology
Jing-Hang Ma, Shang-Feng You, Ji-Sen Xue, Xiao-Lin Li, Yi-Yao Chen, Yan Hu, Zhen Feng
Summary: Computer-aided diagnosis system plays an important role in cervical lesion diagnosis by using auto-segmented colposcopic images to extract features, augmenting minority data, and generating preliminary diagnosis results. The system improves sensitivity while maintaining acceptable specificity and accuracy.
FRONTIERS IN ONCOLOGY
(2022)
Article
Health Care Sciences & Services
Omneya Attallah
Summary: The accurate and rapid detection of novel coronavirus is crucial to prevent its spread, and artificial intelligence techniques can aid in this process. This study proposes a computer-assisted diagnostic framework based on deep learning and texture-based radiomics approaches. By fusing deep features from multiple convolutional neural networks, the diagnostic accuracy is improved. The performance of this framework allows radiologists to achieve fast and accurate diagnosis of coronavirus.
Review
Computer Science, Artificial Intelligence
Parita Oza, Paawan Sharma, Samir Patel, Pankaj Kumar
Summary: This study presents the applications of deep learning networks in computer-aided breast cancer diagnosis and discusses the recent breakthroughs and challenges in this field. The paper provides key insights and novel perspectives on the use of deep convolutional neural networks for mammogram analysis, along with detailed explanations of the latest deep learning toolkits and libraries. The possible limitations in using these networks for breast cancer detection are also pointed out, and future research directions are proposed.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Radiology, Nuclear Medicine & Medical Imaging
Akiyoshi Hizukuri, Ryohei Nakayama, Mayumi Nara, Megumi Suzuki, Kiyoshi Namba
Summary: This study aimed to develop a computer-aided diagnosis scheme for distinguishing benign and malignant breast masses on dynamic contrast material-enhanced MRI using a deep convolutional neural network with Bayesian optimization. The proposed DCNN model achieved high classification performance and outperformed the conventional method based on handcrafted features and a classifier.
JOURNAL OF DIGITAL IMAGING
(2021)
Article
Engineering, Biomedical
Varun Gupta, Megha Vasudev, Amit Doegar, Nitigya Sambyal
Summary: Researchers propose a breast cancer detection method based on a modified residual neural network, which performs well and provides high accuracy diagnosis at various magnification factors.
BIOCYBERNETICS AND BIOMEDICAL ENGINEERING
(2021)
Article
Oncology
Ching-Hsuan Chen, Yen-Shen Lu, Ann-Lii Cheng, Chiun-Sheng Huang, Wen-Hung Kuo, Ming-Yang Wang, Ming Chao, I-Chun Chen, Chun-Wei Kuo, Tzu-Pin Lu, Ching-Hung Lin
Article
Oncology
Sara A. Hurvitz, Miguel Martin, Michael F. Press, David Chan, Maria Fernandez-Abad, Edgar Petru, Regan Rostorfer, Valentina Guarneri, Chiun-Sheng Huang, Susana Barriga, Sameera Wijayawardana, Manisha Brahmachary, Philip J. Ebert, Anwar Hossain, Jiangang Liu, Adam Abel, Amit Aggarwal, Valerie M. Jansen, Dennis J. Slamon
CLINICAL CANCER RESEARCH
(2020)
Article
Oncology
Po-Han Lin, Ming Chen, Li-Wei Tsai, Chiao Lo, Tzu-Chun Yen, Thomas Yoyan Huang, Chih-Kai Chen, Sheng-Chih Fan, Sung-Hsin Kuo, Chiun-Sheng Huang
Article
Multidisciplinary Sciences
Maria Escala-Garcia, Jean Abraham, Irene L. Andrulis, Hoda Anton-Culver, Volker Arndt, Alan Ashworth, Paul L. Auer, Paivi Auvinen, Matthias W. Beckmann, Jonathan Beesley, Sabine Behrens, Javier Benitez, Marina Bermisheva, Carl Blomqvist, William Blot, Natalia V. Bogdanova, Stig E. Bojesen, Manjeet K. Bolla, Anne-Lise Borresen-Dale, Hiltrud Brauch, Hermann Brenner, Sara Y. Brucker, Barbara Burwinkel, Carlos Caldas, Federico Canzian, Jenny Chang-Claude, Stephen J. Chanock, Suet-Feung Chin, Christine L. Clarke, Fergus J. Couch, Angela Cox, Simon S. Cross, Kamila Czene, Mary B. Daly, Joe Dennis, Peter Devilee, Janet A. Dunn, Alison M. Dunning, Miriam Dwek, Helena M. Earl, Diana M. Eccles, A. Heather Eliassen, Carolina Ellberg, D. Gareth Evans, Peter A. Fasching, Jonine Figueroa, Henrik Flyger, Manuela Gago-Dominguez, Susan M. Gapstur, Montserrat Garcia-Closas, Jose A. Garcia-Saenz, Mia M. Gaudet, Angela George, Graham G. Giles, David E. Goldgar, Anna Gonzalez-Neira, Mervi Grip, Pascal Guenel, Qi Guo, Christopher A. Haiman, Niclas Hakansson, Ute Hamann, Patricia A. Harrington, Louise Hiller, Maartje J. Hooning, John L. Hopper, Anthony Howell, Chiun-Sheng Huang, Guanmengqian Huang, David J. Hunter, Anna Jakubowska, Esther M. John, Rudolf Kaaks, Pooja Middha Kapoor, Renske Keeman, Cari M. Kitahara, Linetta B. Koppert, Peter Kraft, Vessela N. Kristensen, Diether Lambrechts, Loic Le Marchand, Flavio Lejbkowicz, Annika Lindblom, Jan Lubinski, Arto Mannermaa, Mehdi Manoochehri, Siranoush Manoukian, Sara Margolin, Maria Elena Martinez, Tabea Maurer, Dimitrios Mavroudis, Alfons Meindl, Roger L. Milne, Anna Marie Mulligan, Susan L. Neuhausen, Heli Nevanlinna, William G. Newman, Andrew F. Olshan, Janet E. Olson, Hakan Olsson, Nick Orr, Paolo Peterlongo, Christos Petridis, Ross L. Prentice, Nadege Presneau, Kevin Punie, Dhanya Ramachandran, Gad Rennert, Atocha Romero, Mythily Sachchithananthan, Emmanouil Saloustros, Elinor J. Sawyer, Rita K. Schmutzler, Lukas Schwentner, Christopher Scott, Jacques Simard, Christof Sohn, Melissa C. Southey, Anthony J. Swerdlow, Rulla M. Tamimi, William J. Tapper, Manuel R. Teixeira, Mary Beth Terry, Heather Thorne, Rob A. E. M. Tollenaar, Ian Tomlinson, Melissa A. Troester, Therese Truong, Clare Turnbull, Celine M. Vachon, Lizet E. van der Kolk, Qin Wang, Robert Winqvist, Alicja Wolk, Xiaohong R. Yang, Argyrios Ziogas, Paul D. P. Pharoah, Per Hall, Lodewyk F. A. Wessels, Georgia Chenevix-Trench, Gary D. Bader, Thilo Doerk, Douglas F. Easton, Sander Canisius, Marjanka K. Schmidt
NATURE COMMUNICATIONS
(2020)
Article
Oncology
PierFranco Conte, Andreas Schneeweiss, Sibylle Loibl, Eleftherios P. Mamounas, Gunter von Minckwitz, Max S. Mano, Michael Untch, Chiun-Sheng Huang, Norman Wolmark, Priya Rastogi, Veronique D'Hondt, Andres Redondo, Ljiljana Stamatovic, Herve Bonnefoi, Hugo Castro-Salguero, Hans H. Fischer, Tanya Wahl, Chunyan Song, Thomas Boulet, Peter Trask, Charles E. Geyer
Article
Environmental Sciences
Meng-shan Tsai, Shu-Han Chang, Wen-Hung Kuo, Ching-Hua Kuo, Szu-Yi Li, Ming-Yang Wang, Dwan-Ying Chang, Yen-Shen Lu, Chiun-Sheng Huang, Ann-Lii Cheng, Ching-Hung Lin, Pau-Chung Chen
ENVIRONMENT INTERNATIONAL
(2020)
Article
Biochemical Research Methods
Chiao Lo, Ya-Lin Hsu, Chih-Ning Cheng, Ching-Hung Lin, Han-Chun Kuo, Chiun-Sheng Huang, Ching-Hua Kuo
JOURNAL OF PROTEOME RESEARCH
(2020)
Article
Computer Science, Interdisciplinary Applications
Jie-Fan Chang, Chiun-Sheng Huang, Ruey-Feng Chang
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2020)
Article
Biology
Yan-Wei Lee, Chiun-Sheng Huang, Chung-Chih Shih, Ruey-Feng Chang
Summary: The deep learning-based computer-aided prediction system for ultrasound images can effectively predict the axillary lymph node metastatic status in patients with early-stage breast cancer. The proposed model combining primary tumor and peritumoral tissue is an effective method for this prediction.
COMPUTERS IN BIOLOGY AND MEDICINE
(2021)
Article
Cardiac & Cardiovascular Systems
Kuan-Chih Huang, Chiun-Sheng Huang, Mao-Yuan Su, Chung-Lieh Hung, Yi-Chin Ethan Tu, Lung-Chun Lin, Juey-Jen Hwang
Summary: The study developed an artificial intelligence tool to assess echocardiographic image quality objectively and found that classification confidence can serve as a quality marker, important for accurate diagnosis of cardiac dysfunction. Patients with higher classification confidence showed higher reliability in echocardiographic strain analysis, leading to a lower false positive detection rate.
JACC-CARDIOVASCULAR IMAGING
(2021)
Article
Oncology
Chiun-Sheng Huang, Youngsen Yang, Ava Kwong, Shin-Cheh Chen, Ling-Ming Tseng, Mei-Ching Liu, Kunwei Shen, Shusen Wang, Ting-Ying Ng, Yi Feng, Guofang Sun, Iris Renfei Yan, Zhimin Shao
Summary: In the KATHERINE study, Chinese patients receiving T-DM1 showed improved efficacy compared to trastuzumab, but experienced more adverse events, primarily driven by thrombocytopenia.
BREAST CANCER RESEARCH AND TREATMENT
(2021)
Article
Oncology
Zhimin Shao, Ling-Ming Tseng, Chiun-Sheng Huang, Da Pang, Youngsen Yang, Wei Li, Ning Liao, Cuizhi Geng, Qingyuan Zhang, Binghe Xu, Donggeng Liu, Ava Kwong, Mandy Yu, Guofang Sun, Volker Mobus, Susan Dent, Amal Arahmani, Gillian Borthwick, Frederic Henot, Gunter von Minckwitz, Zefei Jiang
Summary: In the APHINITY trial, the addition of pertuzumab to trastuzumab and chemotherapy showed significant efficacy for Chinese patients with HER2-positive early breast cancer, with a safety profile consistent with the global population.
JAPANESE JOURNAL OF CLINICAL ONCOLOGY
(2021)
Article
Medicine, General & Internal
Javier Cortes, David W. Cescon, Hope S. Rugo, Zbigniew Nowecki, Seock-Ah Im, Mastura Md Yusof, Carlos Gallardo, Oleg Lipatov, Carlos H. Barrios, Esther Holgado, Hiroji Iwata, Norikazu Masuda, Marco Torregroza Otero, Erhan Gokmen, Sherene Loi, Zifang Guo, Jing Zhao, Gursel Aktan, Vassiliki Karantza, Peter Schmid
Article
Computer Science, Interdisciplinary Applications
Alireza Karimi, Reza Razaghi, Siddharth Daniel D'costa, Saeed Torbati, Sina Ebrahimi, Seyed Mohammadali Rahmati, Mary J. Kelley, Ted S. Acott, Haiyan Gong
Summary: This study investigated the biomechanical properties of the conventional aqueous outflow pathway using fluid-structure interaction. The results showed that the distribution of aqueous humor wall shear stress within this pathway is not uniform, which may contribute to our understanding of the underlying selective mechanisms.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Robert V. Bergen, Jean-Francois Rajotte, Fereshteh Yousefirizi, Arman Rahmim, Raymond T. Ng
Summary: This article introduces a 3D generative model called TrGAN, which can generate medical images with important features and statistical properties while protecting privacy. By evaluating through a membership inference attack, the fidelity, utility, and privacy trade-offs of the model were studied.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Hoda Mashayekhi, Mostafa Nazari, Fatemeh Jafarinejad, Nader Meskin
Summary: In this study, a novel model-free adaptive control method based on deep reinforcement learning (DRL) is proposed for cancer chemotherapy drug dosing. The method models the state variables and control action in their original infinite spaces, providing a more realistic solution. Numerical analysis shows the superior performance of the proposed method compared to the state-of-the-art RL-based approach.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Hao Sun, Bao Li, Liyuan Zhang, Yanping Zhang, Jincheng Liu, Suqin Huang, Xiaolu Xi, Youjun Liu
Summary: In cases of moderate stenosis in the internal carotid artery, the A1 segment of the anterior cerebral artery or the posterior communicating artery within the Circle of Willis may show a hemodynamic environment with high OSI and low TAWSS, increasing the risk of atherosclerosis development and stenosis in the CoW.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Ilaria Toniolo, Paola Pirini, Silvana Perretta, Emanuele Luigi Carniel, Alice Berardo
Summary: This study compared the outcomes of endoscopic sleeve gastroplasty (ESG) and laparoscopic sleeve gastrectomy (LSG) in weight loss surgery using computational models of specific patients. The results showed significant differences between the two procedures in terms of stomach volume reduction and mechanical stimulation. A predictive model was proposed to support surgical planning and estimation of volume reduction after ESG.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Chun-You Chen, Ya-Lin Chen, Jeremiah Scholl, Hsuan-Chia Yang, Yu-Chuan (Jack) Li
Summary: This study evaluated the overall performance of a machine learning-based CDSS (MedGuard) in triggering clinically relevant alerts and intercepting inappropriate drug errors and LASA drug errors. The results showed that MedGuard has the ability to improve patients' safety by triggering clinically valid alerts.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Lingzhi Tang, Xueqi Wang, Jinzhu Yang, Yonghuai Wang, Mingjun Qu, HongHe Li
Summary: In this paper, a dynamical local feature fusion net for automatically recognizing aortic valve calcification (AVC) from echocardiographic images is proposed. The network segments high-echo areas and adjusts the selection of local features to better integrate global and local semantic representations. Experimental results demonstrate the effectiveness of the proposed approach.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
You-Lei Fu, Wu Song, Wanni Xu, Jie Lin, Xuchao Nian
Summary: This study investigates the combination of surface electromyographic signals (sEMG) and deep learning-based CNN networks to study the interaction between humans and products and the impact on body comfort. It compares the advantages and disadvantages of different CNN networks and finds that DenseNet has unique advantages over other algorithms in terms of accuracy and ease of training, while mitigating issues of gradient disappearance and model degradation.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Moritz Rempe, Florian Mentzel, Kelsey L. Pomykala, Johannes Haubold, Felix Nensa, Kevin Kroeninger, Jan Egger, Jens Kleesiek
Summary: In this study, a deep learning-based skull stripping algorithm for MRI was proposed, which works directly in the complex valued k-space and preserves the phase information. The results showed that the algorithm achieved similar results to the ground truth, with higher accuracy in the slices above the eye region. This approach not only preserves valuable information for further diagnostics, but also enables immediate anonymization of patient data before being transformed into the image domain.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Ziyang Chen, Laura Cruciani, Elena Lievore, Matteo Fontana, Ottavio De Cobelli, Gennaro Musi, Giancarlo Ferrigno, Elena De Momi
Summary: In this paper, a deep learning-based approach is proposed to recover 3D information of intra-operative scenes, which can enhance the safety of robot-assisted surgery by implementing depth estimation using stereo images.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Ao Leng, Bolun Zeng, Yizhou Chen, Puxun Tu, Baoxin Tao, Xiaojun Chen
Summary: This study presents a novel training system for zygomatic implant surgery, which offers a more realistic simulation and training solution. By integrating visual, haptic, and auditory feedback, the system achieves global rigid-body collisions and soft tissue simulation, effectively improving surgeons' proficiency.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Yingjie Wang, Xueqing Yin
Summary: This study developed an integrated computational model combining coronary flow and myocardial perfusion models to achieve physiologically accurate simulations. The model has the potential for clinical application in diagnosing insufficient myocardial perfusion.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Nitzan Avidan, Moti Freiman
Summary: This study aims to enhance the generalization capabilities of DNN-based MRI reconstruction methods for undersampled k-space data. By introducing a mask-aware DNN architecture and training method, the under-sampled data and mask are encoded within the model structure, leading to improved performance. Rigorous testing on the widely accessible fastMRI dataset reveals that this approach demonstrates better generalization capabilities and robustness compared to traditional DNN methods.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Enhao Zhang, Saeed Miramini, Lihai Zhang
Summary: This study investigates the combined effects of osteoporosis and diabetes on fracture healing process by developing numerical models. The results show that osteoporotic fractures have higher instability and disruption in mesenchymal stem cells' proliferation and differentiation compared to non-osteoporotic fractures. Moreover, when osteoporosis coexists with diabetes, the healing process of fractures can be severely impaired.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Yunhao Bai, Wenqi Li, Jianpeng An, Lili Xia, Huazhen Chen, Gang Zhao, Zhongke Gao
Summary: This study proposes an effective MIL method for classifying WSI of esophageal cancer. The use of self-supervised learning for feature extractor pretraining enhances feature extraction from esophageal WSI, leading to more robust and accurate performance. The proposed framework outperforms existing methods, achieving an accuracy of 93.07% and AUC of 95.31% on a comprehensive dataset of esophageal slide images.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
