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
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
Computer Science, Artificial Intelligence
Tome Albuquerque, Ricardo Cruz, Jaime S. Cardoso
Summary: Cervical cancer is the fourth leading cause of cancer-related deaths in women, especially in low to middle-income countries. Screening tests have been effective in reducing cervical cancer deaths, with automatic screening via Pap smear being a valuable detection tool.
PEERJ COMPUTER SCIENCE
(2021)
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
Biology
Guangqi Wen, Peng Cao, Huiwen Bao, Wenju Yang, Tong Zheng, Osmar Zaiane
Summary: In this study, a machine learning approach is proposed for the classification of neurological disorders, with an interpretable framework. The results of experiments demonstrate the high classification performance of the proposed method on datasets of Autism Spectrum Disorders and Alzheimer's disease.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Article
Computer Science, Artificial Intelligence
Priyanka Rastogi, Kavita Khanna, Vijendra Singh
Summary: Cervical cancer is a major cause of death for women in developing nations like India. Automated screening systems are crucial for early diagnosis and decision-making in cervical cancer. This study focuses on developing a robust binary classifier capable of classifying single cervical cells as normal or cancerous, using an improved EfficientNet-B7 model and visualization techniques.
Article
Computer Science, Artificial Intelligence
Lei Cao, Jinying Yang, Zhiwei Rong, Lulu Li, Bairong Xia, Chong You, Ge Lou, Lei Jiang, Chun Du, Hongxue Meng, Wenjie Wang, Meng Wang, Kang Li, Yan Hou
Summary: The proposed attention feature pyramid network (AttFPN) for automatic abnormal cervical cell detection in cervical cytology images shows promising performance in clinical practice.
MEDICAL IMAGE ANALYSIS
(2021)
Review
Health Care Sciences & Services
Peng Xue, Jiaxu Wang, Dongxu Qin, Huijiao Yan, Yimin Qu, Samuel Seery, Yu Jiang, Youlin Qiao
Summary: This study conducted a meta-analysis to evaluate the diagnostic performance of deep learning algorithms for early breast and cervical cancer identification. The results showed that these algorithms performed acceptably well across all subgroups, comparable to human clinicians. However, the relatively poor design and reporting of the included studies may have caused bias in the results.
NPJ DIGITAL MEDICINE
(2022)
Article
Genetics & Heredity
Can Liu, Yuchen Duan, Qingqing Zhou, Yongkang Wang, Yong Gao, Hongxing Kan, Jili Hu
Summary: In this study, a gastric cancer subtype classification model called RRGCN was developed based on multi-omics fusion data and patient similarity network using residual graph convolutional network (GCN). The results demonstrate that RRGCN outperforms other classification methods with a high accuracy of 0.87 compared to traditional machine learning methods and deep learning models. Overall, RRGCN shows great potential in providing fresh perspectives on disease mechanisms and progression, and can be valuable for a wide range of disorders and clinical diagnosis.
FRONTIERS IN GENETICS
(2023)
Article
Biochemical Research Methods
Tianyu Wang, Jun Bai, Sheida Nabavi
Summary: The study proposes a multimodal end-to-end deep learning model named sigGCN for cell classification, which combines a graph convolutional network (GCN) and a neural network to exploit gene interaction networks. Results show that the proposed method outperforms other commonly used methods in terms of classification accuracy and F1 scores, demonstrating that integrating prior knowledge about gene interactions with gene expressions using GCN methodologies can extract effective features improving the performance of cell classification.
BMC BIOINFORMATICS
(2021)
Article
Computer Science, Artificial Intelligence
Feng Zhao, Junjie Zhang, Zhe Meng, Hanqiang Liu, Zhenhui Chang, Jiulun Fan
Summary: This article proposes a hybrid network called MVAHN for hyperspectral image (HSI) classification, which combines convolutional neural network (CNN) and transformer structures. It also utilizes a graph convolutional module (GCM) to extract multiple types of feature information. Experimental results show that MVAHN achieves high accuracy on various datasets.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Mathematics
Mohammad Abrar Shakil Sejan, Md Habibur Rahman, Md Abdul Aziz, Jung-In Baik, Young-Hwan You, Hyoung-Kyu Song
Summary: In this paper, a graph convolutional network (GCN) model is proposed to enhance the performance of node classification tasks. A GCN layer is designed by updating the aggregation function using an updated value of the weight coefficient. The aggregation function is calculated using the adjacency matrix of the input graph and the identity matrix. Extensive experimental studies with seven publicly available datasets validate the proposed model, which achieves comparable results with the state-of-the-art methods. The proposed approach can achieve superior results with one single layer.
Article
Computer Science, Artificial Intelligence
Youyi Song, Jing Zou, Kup-Sze Choi, Baiying Lei, Jing Qin
Summary: Cell classification is crucial for intelligent cervical cancer screening, but the variation in cells' appearance and shape poses challenges. A new learning algorithm, worse-case boosting, is proposed to improve classification accuracy for under-represented data. Experimental results demonstrate the effectiveness of this algorithm in two publicly available datasets, achieving a 4% improvement in accuracy.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Biochemical Research Methods
Kangwei Wang, Zhengwei Li, Zhu-Hong You, Pengyong Han, Ru Nie
Summary: In this study, we propose an adversarial dense graph convolutional network architecture for feature learning in single-cell classification. We introduce dense connectivity mechanism and attention-based feature aggregation to enhance the representation of higher-order features and the organic combination between features. A feature reconstruction module is used to preserve the features of the original data and assist in single-cell classification. Experimental results show that our model outperforms existing classical methods in terms of classification accuracy on benchmark datasets.
Article
Computer Science, Information Systems
Yingying Wan, Changan Yuan, Mengmeng Zhan, Long Chen
Summary: This paper proposes a robust graph learning convolutional network (RGLCN) to address the issue of noisy fixed graphs in graph convolutional network (GCN). By designing a robust graph learning model based on sparse constraint and strong connectivity constraint, high-quality graph learning is achieved and integrated into GCN, improving the performance of node classification tasks.
INFORMATION PROCESSING & MANAGEMENT
(2022)
Article
Computer Science, Interdisciplinary Applications
Yushan Zheng, Zhiguo Jiang, Fengying Xie, Jun Shi, Haopeng Zhang, Jianguo Huai, Ming Cao, Xiaomiao Yang
Summary: The development of remote tumor diagnoses through telepathology has been accelerated by the advancement in whole slide imaging techniques and online digital pathology platforms. A novel computer-assisted cancer diagnosis approach, named Session-based Histopathology Image Recommendation (SHIR), based on the browsing paths on WSIs, has been proposed in this article. By developing a Diagnostic Regions Attention Network (DRA-Net), the SHIR framework has shown effective results in recommending diagnostically relevant cases on a gastric dataset.
IEEE TRANSACTIONS ON MEDICAL IMAGING
(2021)
Article
Computer Science, Information Systems
Yushan Zheng, Zhiguo Jiang, Haopeng Zhang, Fengying Xie, Dingyi Hu, Shujiao Sun, Jun Shi, Chenghai Xue
Summary: Color consistency is crucial for developing robust deep learning methods for histopathological image analysis. A novel color standardization module has been proposed in this paper to generate uniform stain separation outputs for histopathological images. The experimental results have demonstrated that the proposed module is effective in improving the performance of histopathological image analysis.
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
(2021)
Article
Computer Science, Information Systems
Zhang Li, Jiehua Zhang, Tao Tan, Xichao Teng, Xiaoliang Sun, Hong Zhao, Lihong Liu, Yang Xiao, Byungjae Lee, Yilong Li, Qianni Zhang, Shujiao Sun, Yushan Zheng, Junyu Yan, Ni Li, Yiyu Hong, Junsu Ko, Hyun Jung, Yanling Liu, Yu-cheng Chen, Ching-wei Wang, Vladimir Yurovskiy, Pavel Maevskikh, Vahid Khanagha, Yi Jiang, Li Yu, Zhihong Liu, Daiqiang Li, Peter J. Schueffler, Qifeng Yu, Hui Chen, Yuling Tang, Geert Litjens
Summary: The ACDC@LungHP challenge evaluated various computer-aided diagnosis methods for automatic detection and classification of lung cancer in pathology slides, with a focus on deep learning techniques. The study found that multi-model methods were significantly superior to single model methods in lung cancer segmentation.
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
(2021)
Article
Biology
Yiguang Yang, Juncheng Wang, Fengying Xie, Jie Liu, Chang Shu, Yukun Wang, Yushan Zheng, Haopeng Zhang
Summary: This study trained an efficient deep-learning network using EfficientNet-B4 architecture to recognize dermoscopic images of psoriasis, achieving accurate classification comparable to dermatologists' diagnostic performance.
COMPUTERS IN BIOLOGY AND MEDICINE
(2021)
Article
Computer Science, Artificial Intelligence
Yilan Zhang, Fengying Xie, Xuedong Song, Yushan Zheng, Jie Liu, Juncheng Wang
Summary: In this study, a dermoscopic image retrieval algorithm using convolutional neural networks and hash coding is designed. A hybrid dilated convolution spatial attention module and a Cauchy rotation invariance loss function are proposed to enhance the performance of the retrieval. Extensive experiments validate the effectiveness and versatility of the proposed module, algorithm, and loss function in dermoscopic image retrieval.
MEDICAL IMAGE ANALYSIS
(2022)
Article
Computer Science, Artificial Intelligence
Yushan Zheng, Zhiguo Jiang, Jun Shi, Fengying Xie, Haopeng Zhang, Wei Luo, Dingyi Hu, Shujiao Sun, Zhongmin Jiang, Chenghai Xue
Summary: Content-based histopathological image retrieval (CBHIR) systems have become popular for providing auxiliary diagnosis information for pathologists by searching and returning regions similar to the region of interest (ROI) in a database of histopathological whole slide images (WSIs). In this paper, a novel framework based on location-aware graphs and deep hash techniques was proposed for region retrieval from the WSI database, preserving structural and global location information of ROIs. The experimental results demonstrated superior performance in irregular region retrieval tasks, with high average precision and efficient retrieval time.
MEDICAL IMAGE ANALYSIS
(2022)
Article
Cardiac & Cardiovascular Systems
Feng Wang, Lefei Zhou, Zhen Wang, Lili Xu, Yanbing Wu, Xue Li, Xiaojian Qiu, Songlin Zhao, Yushan Zheng, Zhiguo Jiang, Huanzhong Shi, Zhaohui Tong
Summary: This study evaluated the clinical usefulness of autofluorescence imaging (AFI) for the diagnosis of malignant pleural diseases. The results showed that AFI had high sensitivity and accuracy in diagnosing malignant pleural diseases.
RESPIRATORY MEDICINE
(2023)
Article
Computer Science, Interdisciplinary Applications
Yushan Zheng, Jun Li, Jun Shi, Fengying Xie, Jianguo Huai, Ming Cao, Zhiguo Jiang
Summary: In this paper, a kernel attention Transformer (KAT) is proposed for histopathology whole slide image (WSI) analysis and cancer diagnosis. Compared to the common Transformer, KAT can extract more contextual information and has lower computational cost.
IEEE TRANSACTIONS ON MEDICAL IMAGING
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Jun Shi, Xinyu Zhu, Yuan Zhang, Yushan Zheng, Zhiguo Jiang, Liping Zheng
Summary: In this paper, a novel cervical cell classification method based on multi-scale feature fusion and channel-wise cross-attention is proposed. The method combines multi-scale cell features from the perspective of channels and explores channel dependencies and non-local semantic information through multi-head channel-wise cross-attention. The effectiveness of the method is demonstrated through experiments on three public cervical cell datasets.
2023 IEEE 20TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING, ISBI
(2023)
Proceedings Paper
Engineering, Biomedical
Dingyi Hu, Fengying Xie, Zhiguo Jiang, Yushan Zheng, Jun Shi
Summary: This paper proposes a cross-modal retrieval framework based on histopathology WSIs and diagnosis reports, which can simultaneously achieve four retrieval tasks for histopathology database across WSIs and diagnosis reports. The method has been verified to be effective in cross-modal retrieval tasks for digital pathology system.
2022 IEEE 22ND INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOENGINEERING (BIBE 2022)
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Yushan Zheng, Jun Li, Jun Shi, Fengying Xie, Zhiguo Jiang
Summary: This paper proposes a kernel attention Transformer (KAT) for histopathology WSI classification. Compared to the common Transformer structure, KAT can better describe the hierarchical context information of the local regions of the WSI and maintains a lower computational complexity. Experimental results show that KAT is effective and efficient in histopathology WSI classification and outperforms 6 state-of-the-art methods.
MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION, MICCAI 2022, PT II
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Jun Li, Yushan Zheng, Kun Wu, Jun Shi, Fengying Xie, Zhiguo Jiang
Summary: This paper proposes a novel contrastive representation learning framework called LACL for histopathological whole slide image analysis. By building a lesion queue and designing a queue refinement strategy, it achieves the best performance on different datasets.
MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION, MICCAI 2022, PT II
(2022)
Proceedings Paper
Computer Science, Information Systems
Jun Li, Zhiguo Jiang, Yushan Zheng, Haopeng Zhang, Jun Shi, Dingyi Hu, Wei Luo, Zhongmin Jiang, Chenghai Xue
Summary: This paper proposes a weakly supervised framework for learning feature representations of lesion areas from histopathology whole slide images. The experimental results demonstrate that the proposed method can effectively learn discriminative features for histopathology image classification and achieve performance close to fully supervised learning methods.
MEDICAL IMAGING 2022: DIGITAL AND COMPUTATIONAL PATHOLOGY
(2022)
Proceedings Paper
Engineering, Biomedical
Jun Shi, Kun Wu, Yushan Zheng, Yuxin He, Jun Li, Zhiguo Jiang, Lanlan Yu
Summary: In this paper, a global-local network is proposed for weakly supervised cervical cytology region of interest (ROI) analysis. The method combines attention mechanism and deep learning to achieve automatic whole slide cervical image analysis and alleviate the workload of cytologists with weakly supervised image-level labels.
2022 IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (IEEE ISBI 2022)
(2022)
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)