Article
Computer Science, Artificial Intelligence
Honglei He, Yuxuan He, Fang Wang, Wenming Zhu
Summary: In this study, an improved K-means algorithm (IK-means) is proposed to enhance clustering efficiency for non-spherical data. By clustering the original dataset into high-density sub-clusters and merging them, IK-means algorithm shows good clustering capability for data of arbitrary shape and is faster for larger datasets compared to DBSCAN and KGFCM.
Article
Computer Science, Theory & Methods
Michail Tsagris, Abdulaziz Alenazi, Connie Stewart
Summary: This article presents a non-parametric regression approach for analyzing compositional data, using an extension of k-Nearest Neighbours and kernel regression methods, which can accommodate zero values. Simulation studies and real-life data analyses demonstrate that these non-parametric regression methods can make more accurate predictions for complex relationships between compositional response data and Euclidean predictor variables.
STATISTICS AND COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Imran Khan, Zongwei Luo, Abdul Khalique Shaikh, Rachid Hedjam
Summary: In this paper, a new ensemble clustering method is proposed, which incorporates two new steps in the standard fuzzy k-means algorithm to determine the optimal number of input clusterings and the optimal number of clusters in each clustering. Experiments show that the proposed algorithm outperformed well-known clustering algorithms in real cancer gene expression profiles.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Shudong Huang, Zhao Kang, Zenglin Xu, Quanhui Liu
Summary: Clustering aims to divide input data into different groups based on distance or similarity, with k-means being a widely used method. A deep k-means model is proposed in this study to improve clustering performance by extracting deep representations using deep learning techniques.
PATTERN RECOGNITION
(2021)
Article
Computer Science, Artificial Intelligence
Mustafa Jahangoshai Rezaee, Milad Eshkevari, Morteza Saberi, Omar Hussain
Summary: This paper introduces a game-based k-means (GBK-means) algorithm that competes cluster centers to attract data for more accurate clustering. Experimental results demonstrate the superiority of GBK-means over traditional clustering algorithms.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Information Systems
Xia Sheng, Qi Zhang, Ran Gao, Dong Guo, Zexuan Jing, Xiangjun Xin
Summary: The study introduces a novel approach to enhance capacity and anti-interference capability against nonlinear noise by applying the K-means clustering algorithm, showing a significant reduction in the gap between IPM and Shannon limit.
Article
Computer Science, Artificial Intelligence
Marco Loog, Jesse H. H. Krijthe, Manuele Bicego
Summary: Recently, the issue of whether a learner's performance improves with more training data has received renewed attention. Surprising findings have shown that more data does not necessarily lead to improved performance. This paper explores the same issue in the context of k-means clustering and demonstrates that it can also suffer from a lack of monotonicity and deteriorate in expected performance. Theoretical contributions show that 1-means clustering is monotonic while 2-means clustering does not even weakly satisfy monotonicity.
Article
Computer Science, Artificial Intelligence
Shuyin Xia, Daowan Peng, Deyu Meng, Changqing Zhang, Guoyin Wang, Elisabeth Giem, Wei Wei, Zizhong Chen
Summary: This paper presents a novel accelerated exact k-means algorithm called Ball k-means, which uses a ball to describe each cluster. The algorithm focuses on reducing the point-centroid distance computation by finding neighbor clusters for each cluster. It divides each cluster into stable and active areas, with the latter further divided into annular areas. The points in the stable area remain unchanged, while the points in each annular area are adjusted among a few neighbor clusters. The Ball k-means achieves higher performance and requires fewer distance calculations compared to other state-of-the-art accelerated exact bounded methods, making it a versatile replacement for the naive k-means algorithm.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2022)
Review
Computer Science, Interdisciplinary Applications
Sarah Mullin, Jaroslaw Zola, Robert Lee, Jinwei Hu, Brianne MacKenzie, Arlen Brickman, Gabriel Anaya, Shyamashree Sinha, Angie Li, Peter L. Elkin
Summary: This study utilized k-means methods to subtype opioid use trajectories from EHR data and interpreted the resulting subtypes using decision trees. Finally, the discussion focused on incorporating these subtypes as features in static machine learning models for predicting opioid overdose and adverse events.
JOURNAL OF BIOMEDICAL INFORMATICS
(2021)
Article
Computer Science, Artificial Intelligence
Carlo Baldassi
Summary: We introduce an evolutionary algorithm called recombinator-k-means for optimizing the highly nonconvex kmeans problem. Its defining feature is that its crossover step involves all the members of the current generation, stochastically recombining them with a repurposed variant of the k-means++ seeding algorithm. The recombination also uses a reweighting mechanism that realizes a progressively sharper stochastic selection policy and ensures that the population eventually coalesces into a single solution. We compare this scheme with a state-of-the-art alternative, a more standard genetic algorithm with deterministic pairwise-nearest-neighbor crossover and an elitist selection policy, of which we also provide an augmented and efficient implementation. Extensive tests on large and challenging datasets (both synthetic and real word) show that for fixed population sizes recombinator-k-means is generally superior in terms of the optimization objective, at the cost of a more expensive crossover step. When adjusting the population sizes of the two algorithms to match their running times, we find that for short times the (augmented) pairwise-nearest-neighbor method is always superior, while at longer times recombinator-k-means will match it and, on the most difficult examples, take over. We conclude that the reweighted whole-population recombination is more costly but generally better at escaping local minima Moreover, it is algorithmically simpler and more general (it could be applied even to k-medians or k-medoids, for example).
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2022)
Article
Multidisciplinary Sciences
Yilin Wan, Qi Xiong, Zhiwei Qiu, Yaohan Xie
Summary: This paper proposes a data clustering approach based on MCSSA, which initializes the centroids' positions using a memristive chaotic system and combines with the K-means algorithm for data clustering. Empirical research confirms the effectiveness and feasibility of this method.
Article
Biochemical Research Methods
Juho Timonen, Henrik Mannerstrom, Aki Vehtari, Harri Lahdesmaki
Summary: Longitudinal study designs are crucial for studying disease progression, but interpreting covariate effects from such data is challenging. The proposed method lgpr outperforms previous approaches in identifying relevant covariates and incorporates features to handle covariate heterogeneity and temporal uncertainty in biomedical data analysis. The tool is implemented as a comprehensive and user-friendly R-package.
Article
Computer Science, Artificial Intelligence
Rustam Mussabayev, Nenad Mladenovic, Bassem Jarboui, Ravil Mussabayev
Summary: K-means plays a vital role in data mining and is the simplest and most widely used algorithm under the Euclidean Minimum Sum-of-Squares Clustering (MSSC) model. However, its performance drastically drops when applied to vast amounts of data. Therefore, it is crucial to improve K-means by scaling it to big data using as few computational resources as possible. We propose a new parallel scheme of using K-means and K-means++ algorithms for big data clustering that outperforms the classical and recent state-of-the-art approaches in terms of solution quality and runtime.
PATTERN RECOGNITION
(2023)
Review
Computer Science, Information Systems
Abiodun M. Ikotun, Absalom E. Ezugwu, Laith Abualigah, Belal Abuhaija, Jia Heming
Summary: Advances in data collection techniques have enabled the accumulation of large quantities of data. The K-means algorithm, while popular, has challenges such as determining the number of clusters and detecting non-Euclidean shapes. Research efforts have been made to improve its performance and robustness.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Haize Hu, Jianxun Liu, Xiangping Zhang, Mengge Fang
Summary: In this paper, a novel k-means clustering algorithm based on Levy flight trajectory (Lk-means) is proposed to address the shortcomings of the traditional k-means algorithm. Experimental results show that LK-means algorithm outperforms other algorithms in terms of search results and distribution of cluster centroids, significantly improving the global search ability, big data processing capacity, and even distribution of cluster centroids of the K-means algorithm.
PATTERN RECOGNITION
(2023)
Article
Psychology, Clinical
Vincent Begin, Nathalie M. G. Fontaine, Frank Vitaro, Michel Boivin, Richard E. Tremblay, Sylvana M. Cote
Summary: This study aimed to identify the perinatal and early-life factors associated with the development of psychopathic traits in childhood. The results showed that psychotropic exposures during pregnancy, socioeconomic adversity, child's aggression and opposition, mother's depressive symptoms, and hostile parenting were all associated with an increase in psychopathic traits.
PSYCHOLOGICAL MEDICINE
(2023)
Article
Psychology, Clinical
Melissa Commisso, Caroline Temcheff, Massimiliano Orri, Martine Poirier, Marianne Lau, Sylvana Cote, Frank Vitaro, Gustavo Turecki, Richard Tremblay, Marie-Claude Geoffroy
Summary: This study found that childhood externalizing problems and comorbid problems were associated with suicide attempts, while internalizing problems were not associated with suicidal ideation. This suggests that these problems confer a specific risk for suicide attempts.
PSYCHOLOGICAL MEDICINE
(2023)
Article
Sport Sciences
Maher Souabni, Omar Hammouda, Mehdi J. Souabni, Mohamed Romdhani, Tarak Driss
Summary: The study suggests that a 40-minute nap opportunity can reduce sleepiness and stress and fatigue, while enhancing physical outcomes of specific skills in elite basketball players.
RESEARCH IN SPORTS MEDICINE
(2023)
Article
Sport Sciences
Omar Boukhris, Khaled Trabelsi, David W. Hill, Hsen Hsouna, Raouf Abdessalem, Achraf Ammar, Omar Hammouda, Cain C. T. Clark, Piotr Zmijewski, Peter Duking, Tarak Driss, Hamdi Chtourou
Summary: This study aimed to investigate the effects of different nap durations on exercise performance and physiological/perceptual measures. The results showed that napping improved running performance and longer naps were more effective.
RESEARCH IN SPORTS MEDICINE
(2023)
Article
Hospitality, Leisure, Sport & Tourism
Mohamed Amine Farjallah, Ahmed Graja, Kais Ghattassi, Lobna Ben Mahmoud, Henda Elleuch, Fatma Ayadi, Tarak Driss, Kamel Jammoussi, Zouheir Sahnoun, Nizar Souissi, Omar Hammouda
Summary: This study suggests that ingestion of melatonin (MEL) before maximal running exercise may be beneficial in protecting athletes from liver damage and perturbation in renal function biomarkers. However, further research is needed to assess the chronic effects and circadian rhythm.
RESEARCH QUARTERLY FOR EXERCISE AND SPORT
(2023)
Article
Medicine, General & Internal
Francis Vergunst, Melissa Commisso, Marie-Claude Geoffroy, Caroline Temcheff, Martine Poirier, Jungwee Park, Frank Vitaro, Richard Tremblay, Sylvana Cote, Massimilliano Orri
Summary: This study used 32 years of longitudinal data and found that children with externalizing, internalizing, or comorbid symptoms during school age were at increased risk for poor economic and social outcomes in the long term. Especially, children exhibiting comorbid problems were particularly vulnerable. Early detection and support are crucial for these children.
Article
Psychology, Educational
Catalina Rey-Guerra, Henrik D. Zachrisson, Eric Dearing, Daniel Berry, Susanne Kuger, Margaret R. Burchinal, Ane Naerde, Thomas van Huizen, Sylvana M. Cote
Summary: The question of whether high quantities of center-based care cause behavior problems is controversial. Studies using covariate adjustment for selection factors have found a relation between center care and behavior problems, but studies with stronger internal validity less frequently find evidence of such a relationship. A meta-analysis of seven studies examining changes in hours in center-based care and changes in externalizing problems in toddlers and preschoolers found no association between the two variables.
Article
Education & Educational Research
Rene Carbonneau, Richard E. Tremblay, Frank Vitaro, Mara Brendgen, Michel Boivin, Pascale Domond, Sylvana Cote
Summary: This study explores the patterns of relative academic achievement of children in the classroom from grade 1 to grade 6 and their associations with child, parental, and socio-familial characteristics. The findings indicate that low parental education and family income, male sex, and poor parental behaviors and attitudes towards the child are associated with a lower trajectory of relative academic achievement.
EARLY CHILDHOOD RESEARCH QUARTERLY
(2023)
Article
Psychology
Leonard Frach, Eshim S. S. Jami, Tom A. A. McAdams, Frank Dudbridge, Jean-Baptiste Pingault
Summary: Identifying early causal factors is crucial for developing effective preventive interventions for poor mental health and behavioral outcomes. Parental risk factors, such as maternal stress during pregnancy, parental education, parental psychopathology, and parent-child relationship, are significantly associated with child outcomes, highlighting the importance of parental influence. However, these associations may also be influenced by confounding factors, such as genetic transmission. Observational studies can help infer causality, and this review provides an overview of current causal inference methods in intergenerational settings, including genetically informed and analytical methods. The review discusses their application to child mental health and outlines future research areas for investigating the causal nature of intergenerational effects.
PSYCHOLOGICAL REVIEW
(2023)
Article
Multidisciplinary Sciences
Lucy Karwatowska, Leonard Frach, Tabea Schoeler, Jorim J. Tielbeek, Joseph Murray, Eco de Geus, Essi Viding, Jean-Baptiste Pingault
Summary: Observational studies have found an association between low resting heart rate (RHR) and higher levels of antisocial behavior (ASB), but it is unclear if this represents a causal relationship. To investigate further, the study conducted various genetic analyses but found no evidence of a causal association between RHR and ASB. The findings suggest that individual differences in autonomic nervous system functioning indexed by RHR are unlikely to directly contribute to the development of ASB.
SCIENTIFIC REPORTS
(2023)
Article
Psychology, Developmental
Edith Breton, Sylvana M. Cote, Lise Dubois, Frank Vitaro, Michel Boivin, Richard E. Tremblay, Linda Booij
Summary: Eating disorders have early origins and there may be a link between childhood eating behaviors and long-term disordered eating. Factors such as BMI, desire for thinness, and peer victimization could influence this link, but the exact mechanisms are unknown. To address this knowledge gap, the study used data from the Quebec Longitudinal Study of Child Development and found that a significant percentage of youth had a trajectory of high disordered eating from 12 to 20 years old. The findings highlight the importance of promoting healthy body images and eating behaviors among young people.
JOURNAL OF YOUTH AND ADOLESCENCE
(2023)
Article
Criminology & Penology
Adam Vanzella-Yang, Yann Algan, Elizabeth Beasley, Sylvana Cote, Frank Vitaro, Richard E. Tremblay, Jungwee Park
Summary: The 2-year intervention program in Montreal aimed at improving social skills and self-control among disruptive boys from low-income neighborhoods resulted in improved behavioral indicators, increased high school graduation rates, reduced crime rates, and better labor market outcomes in adulthood. Importantly, the program demonstrated considerable cost-effectiveness and generated positive returns on taxpayer investments.
CRIMINAL BEHAVIOUR AND MENTAL HEALTH
(2023)
Article
Psychology, Clinical
Nina Pocuca, Marie-Claude Geoffroy, Stephane Paquin, Kim Archambault, Jean R. Seguin, Sophie Parent, Michel Boivin, Richard E. Tremblay, Sylvana Cote, Natalie Castellanos-Ryan
Summary: This study examined the structure of psychopathology in mid-adolescence using symptom dimensions and found that a bifactor model provided the best fit. The bifactor model consisted of a general psychopathology factor and specific internalizing, externalizing, or substance use factors. Additionally, the study found that the general psychopathology factor (P factor) was associated with various mental health disorders and alcohol use disorder at 20 years. The results suggest that targeting the common liability to psychopathology may be important in preventing later mental health problems and AUD.
JOURNAL OF PSYCHOPATHOLOGY AND CLINICAL SCIENCE
(2023)
Review
Nutrition & Dietetics
Khaled Trabelsi, Achraf Ammar, Omar Boukhris, Jordan M. Glenn, Cain C. T. Clark, Stephen R. R. Stannard, Gary Slater, Piotr Zmijewski, Tarak Driss, Helmi Ben Saad, Karim Chamari, Hamdi Chtourou
Summary: This research aimed to evaluate the impact of Ramadan observance on dietary intake and body composition in adult athletes. It was a systematic review and meta-analysis that included nine studies on dietary intake and seventeen studies on body composition. The findings showed a decrease in energy, carbohydrate, and water intake during Ramadan, while fat and protein intake remained unchanged. Body mass and body fat percentage decreased in the fourth week of Ramadan, but lean body mass did not change. Continued training during Ramadan was associated with decreased body mass and body fat percentage towards the end of the fasting month.
JOURNAL OF THE AMERICAN NUTRITION ASSOCIATION
(2023)
Article
Substance Abuse
Rene Carbonneau, Frank Vitaro, Mara Brendgen, Michel Boivin, Sylvana M. Cote, Richard E. Tremblay
Summary: This study investigated the developmental patterns of gambling participation and substance use in adolescents and found six different patterns. The results indicated that gambling and substance use do not influence each other in terms of onset and course throughout adolescence, and do not affect the types of gambling activities or substances used, problems related to gambling or substance use, or substance abuse.
JOURNAL OF GAMBLING STUDIES
(2023)
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)
