Article
Acoustics
Ruiguo Yu, Shaoqi Yan, Jie Gao, Mankun Zhao, Xuzhou Fu, Yang Yan, Ming Li, Xuewei Li
Summary: The objective of this work was to train a semantic segmentation model for thyroid nodule ultrasound images using classification data and improve the segmentation performance by mining image information. A novel foreground and background pair representation method was proposed, along with a self-supervised learning pretext task. Experiments showed that the proposed method outperformed existing methods and achieved accurate segmentation.
ULTRASOUND IN MEDICINE AND BIOLOGY
(2023)
Article
Biology
Haifan Gong, Jiaxin Chen, Guanqi Chen, Haofeng Li, Guanbin Li, Fei Chen
Summary: Ultrasound segmentation of thyroid nodules is challenging due to the lack of thyroid gland region perception and the inherently low contrast images. The current datasets are limited and not representative of real-world situations. To address these limitations, we propose a thyroid region prior guided feature enhancement network (TRFE+) for accurate thyroid nodule segmentation.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Computer Science, Artificial Intelligence
Pushpak Pati, Guillaume Jaume, Zeineb Ayadi, Kevin Thandiackal, Behzad Bozorgtabar, Maria Gabrani, Orcun Goksel
Summary: This study proposes a weakly-supervised method called WholeSIGHT, which can simultaneously segment and classify WSIs of arbitrary shapes and sizes. It achieves state-of-the-art weakly-supervised segmentation performance on three public prostate cancer WSI datasets.
MEDICAL IMAGE ANALYSIS
(2023)
Article
Computer Science, Artificial Intelligence
Zhizhou Li, Shichong Zhou, Cai Chang, Yuanyuan Wang, Yi Guo
Summary: This paper proposes a novel weakly supervised deep active contour model for nodule segmentation in thyroid ultrasound images. The model achieves accurate segmentation results by iteratively deforming an initial contour to match the nodule boundary.
PATTERN RECOGNITION LETTERS
(2023)
Article
Computer Science, Artificial Intelligence
Jun Zhang, Zhiyuan Hua, Kezhou Yan, Kuan Tian, Jianhua Yao, Eryun Liu, Mingxia Liu, Xiao Han
Summary: This paper introduces a weakly-supervised model using joint fully convolutional and graph convolutional networks for automated segmentation of pathology images. By utilizing image-level labels instead of pixel-wise annotations, the segmentation model's performance is improved. Experimental results demonstrate the effectiveness of this method in cancer region segmentation.
MEDICAL IMAGE ANALYSIS
(2021)
Article
Computer Science, Artificial Intelligence
Feng Gao, Minhao Hu, Min-Er Zhong, Shixiang Feng, Xuwei Tian, Xiaochun Meng, Ma-yi-di-li Ni-jia-ti, Zeping Huang, Minyi Lv, Tao Song, Xiaofan Zhang, Xiaoguang Zou, Xiaojian Wu
Summary: This paper proposes a novel weakly- and semi-supervised framework named SOUSA, which aims to learn from a small set of sparse annotated data and a large amount of unlabeled data. Extensive experiments demonstrate the robustness and generalization ability of the proposed method on multiple datasets.
MEDICAL IMAGE ANALYSIS
(2022)
Article
Computer Science, Artificial Intelligence
Lin Sui, Chen-Lin Zhang, Jianxin Wu
Summary: Weakly supervised vision tasks, such as detection and segmentation, have received significant attention recently. However, the lack of detailed and precise annotations in weakly supervised scenarios results in a large accuracy gap compared to fully supervised methods. In this article, we propose a new framework called Salvage of Supervision (SoS) to effectively utilize all potentially useful supervisory signals in weakly supervised vision tasks. By applying SoS-WSOD to weakly supervised object detection, we achieve a significant reduction in the technology gap and overcome the limitations of traditional methods.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Fangfang Lu, Zhihao Zhang, Tianxiang Liu, Chi Tang, Hualin Bai, Guangtao Zhai, Jingjing Chen, Xiaoxin Wu
Summary: Recently, fully supervised learning methods have been successfully used for lung CT image segmentation. However, pixel-wise annotations are demanding and time-consuming, while unsupervised learning methods fail to meet practical requirements. To address this, a novel weakly supervised inpainting-based learning method is introduced, which only requires bounding box labels for accurate segmentation. The method detects lesion regions, recovers missing holes, and applies post-processing for accurate segmentation. Experiments on a COVID-19 dataset demonstrate the outstanding performance of the proposed method in lung CT image inpainting.
PATTERN RECOGNITION
(2023)
Article
Engineering, Civil
Yujie Li, Jiaxing Sun, Yun Li
Summary: A weakly-supervised semantic segmentation network utilizing graph convolution and iterative dCRF was proposed in this study, achieving high accuracy by combining CAMs and node features generated by ResNet and using graph convolution for feature propagation.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Computer Science, Information Systems
Weide Liu, Xiangfei Kong, Tzu-Yi Hung, Guosheng Lin
Summary: Weakly supervised image segmentation trained with image-level labels usually suffer from inaccurate coverage of object areas during pseudo groundtruth generation. To enhance the generality of object activation maps, we propose a region prototypical network (RPNet) to explore cross-image object diversity. Similar object parts are identified through region feature comparison, with object confidence propagated to discover new object areas and suppress background regions. Experiments demonstrate that our approach generates more complete and accurate pseudo object masks and achieves state-of-the-art performance on PASCAL VOC 2012 and MS COCO. We also investigate the robustness of our method on reduced training sets. The code can be accessed at https://github.com/liuweide01/RPNet-Weakly-Supervised-Segmentation.
IEEE TRANSACTIONS ON MULTIMEDIA
(2023)
Article
Environmental Sciences
Jun Chen, Weifeng Xu, Yang Yu, Chengli Peng, Wenping Gong
Summary: This paper proposes a reliable label-supervised pixel attention mechanism for building segmentation in UAV imagery. Experimental results demonstrate that the method outperforms previous weakly supervised methods on a UAV dataset.
Article
Computer Science, Artificial Intelligence
Kazuya Nishimura, Chenyang Wang, Kazuhide Watanabe, Dai Fei Elmer Ker, Ryoma Bise
Summary: Cell instance segmentation is vital in biomedical research, but traditional annotation methods are time-consuming and labor-intensive. Our proposed weakly supervised method reduces annotation cost significantly by using rough cell centroid positions as training data to segment individual cell regions under various conditions.
MEDICAL IMAGE ANALYSIS
(2021)
Article
Computer Science, Artificial Intelligence
Kailu Li, Ziniu Qian, Yingnan Han, Eric I-Chao Chang, Bingzheng Wei, Maode Lai, Jing Liao, Yubo Fan, Yan Xu
Summary: This paper proposes a novel weakly supervised method called SA-MIL for pixel-level segmentation in histopathology images. It introduces a self-attention mechanism and deep supervision to address the lack of relations between instances in multiple instance learning. The approach demonstrates state-of-the-art results and generalization ability on two histopathology image datasets, showing potential for various applications in medical images.
MEDICAL IMAGE ANALYSIS
(2023)
Article
Computer Science, Information Systems
Xiuping Nie, Lilu Liu, Lifeng He, Liang Zhao, Haojian Lu, Songmei Lou, Rong Xiong, Yue Wang
Summary: In order to overcome the lack of large-scale pixel-level annotated datasets in common medical image segmentation tasks, a novel Weakly-Interactive-Mixed Learning (WIML) framework is proposed to achieve the desired segmentation accuracy by efficiently using weak labels. The framework includes a Weakly-Interactive Annotation (WIA) part to reduce annotation time and a Mixed-Supervised Learning (MSL) part to boost segmentation accuracy. A Full-Parameter-Sharing Network (FPSNet) with attention modules (scSE) and a Full-Parameter-Sharing (FPS) strategy are also proposed to implement this framework.
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
(2023)
Article
Computer Science, Artificial Intelligence
Ruoyun Liu, Shichong Zhou, Yi Guo, Yuanyuan Wang, Cai Chang
Summary: A weakly supervised framework called U2F-GAN is proposed for nodule segmentation in thyroid ultrasound images, using only a handful of rough bounding box annotations to generate reliable labels. By alternating between generating masks and learning a segmentation network adversarially, this method effectively removes noise in localization annotations and enhances the network's generalization capability, resulting in a significant performance improvement in segmentation.
COGNITIVE COMPUTATION
(2021)
Article
Engineering, Multidisciplinary
Chao Lin, Pengjun Wang, Xuehua Zhao, Huiling Chen
Summary: The Double Mutation Salp Swarm Algorithm (DMSSA) improves the stability and performance in solving optimization problems by incorporating a Cuckoo Mutation Strategy and an Adaptive DE Mutation Strategy. Comparisons and tests on benchmark functions demonstrate the superiority of DMSSA. Experiments on classical engineering design optimization problems further confirm its applicability and scalability.
JOURNAL OF BIONIC ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Ying Chen, Huimin Gan, Huiling Chen, Yugang Zeng, Liang Xu, Ali Asghar Heidari, Xiaodong Zhu, Yuanning Liu
Summary: Iris segmentation algorithms based on deep learning lack generalization ability and cannot accurately segment iris images without corresponding ground truth data. Normalization is required to reduce the influence of pupil deformation, but it introduces noise in nonconnected iris regions and decreases recognition rate. This paper proposes an end-to-end unified framework based on deep learning that achieves improved accuracy in iris segmentation and recognition without normalization. The framework includes MADNet for iris segmentation and DSANet for iris recognition, and experiments show that it outperforms other methods on low-quality iris images without ground truth data.
Article
Engineering, Biomedical
Meilin Zhang, Qianxi Wu, Huiling Chen, Ali Asghar Heidari, Zhennao Cai, Jiaren Li, Elsaid Md. Abdelrahim, Romany F. Mansour
Summary: This paper proposes an efficient and intelligent prediction model based on the machine learning approach, which combines the improved whale optimization algorithm (RRWOA) with the k-nearest neighbor classifier. The experimental results demonstrate that the model performs well in different test functions and achieves good results compared to other algorithms on the COVID-19 dataset. Therefore, RRWOA is an effectively improved optimizer.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2023)
Article
Engineering, Multidisciplinary
Jiao Hu, Shushu Lv, Tao Zhou, Huiling Chen, Lei Xiao, Xiaoying Huang, Liangxing Wang, Peiliang Wu
Summary: Pulmonary hypertension is a global health problem. This study proposes a model combining the Whale Optimization Algorithm and Kernel Extreme Learning Machine to predict PH mouse models. The selected blood indicators are essential for identifying the models, and the method achieved 100% accuracy and specificity, showing great potential for evaluating and identifying PH mouse models.
JOURNAL OF BIONIC ENGINEERING
(2023)
Article
Engineering, Multidisciplinary
Xin Wang, Xiaogang Dong, Yanan Zhang, Huiling Chen
Summary: This paper proposes a variant of the Harris Hawks Optimizer called Crisscross Harris Hawks Optimizer (CCHHO), which uses the Crisscross Optimization Algorithm (CSO) and shows improved efficiency and convergence on various optimization tasks.
JOURNAL OF BIONIC ENGINEERING
(2023)
Article
Computer Science, Information Systems
Ziwei Huang, Jingjing Zheng, Li Zhao, Huiling Chen, Xianta Jiang, Xiaoqin Zhang
Summary: The purpose of this paper is to determine the grasp type for an object to be grasped, which can be applied to prosthetic hand control and ease the burden of amputees. To enhance the performance of grasp pattern recognition, the authors propose a network DL-Net inspired by dictionary learning. The experimental results show that the DL-Net performs better than traditional deep learning methods in grasp pattern recognition.
Article
Computer Science, Artificial Intelligence
Xiao Yang, Rui Wang, Dong Zhao, Fanhua Yu, Chunyu Huang, Ali Asghar Heidari, Zhennao Cai, Sami Bourouis, Abeer D. Algarni, Huiling Chen
Summary: The sine cosine algorithm (SCA) is a well-known optimization algorithm that has gained attention for its simple structure and excellent optimization capabilities. To overcome the limitations of the original SCA, a modified variant called ARSCA is proposed, which incorporates adaptive quadratic interpolation mechanism and rounding mechanism. Experimental results demonstrate that ARSCA outperforms its competitors in terms of solution quality and ability to escape local optima.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Yingjie Song, Xing Cai, Xiangbing Zhou, Bin Zhang, Huiling Chen, Yuangang Li, Wuquan Deng, Wu Deng
Summary: This paper proposes an optimization algorithm QGDECC, which combines quantum evolutionary algorithm and genetic algorithm, to effectively schedule railway train delay. The algorithm is validated on benchmark functions and actual train operation data, and the results show that QGDECC has higher adaptability and convergence speed, and can effectively eliminate the impact of delay on the railway network.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Hardware & Architecture
Jiaochen Chen, Zhennao Cai, Ali Asghar Heidari, Lei Liu, Huiling Chen, Jingye Pan
Summary: This paper proposes a dynamic mechanism-assisted ABC algorithm (EABC) that improves the convergence speed and optimization performance of the traditional ABC algorithm. The EABC-based MTIS model achieves effective results in COVID-19 X-ray chest image segmentation.
Article
Computer Science, Information Systems
Shuhui Hao, Changcheng Huang, Ali Asghar Heidari, Qike Shao, Huiling Chen
Summary: COVID-19 X-ray images are crucial for diagnosing infections. The use of multi-threshold image segmentation technology can help doctors determine infection and disease progression more efficiently. A strengthened version of the hunger games search algorithm, CDHGS, has been proposed to address the issues of slow convergence and local optimum solutions.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Mathematics, Interdisciplinary Applications
Hui Yang, Chunmei Zhang, Ran Li, Huiling Chen
Summary: This paper focuses on the equilibrium problem of an urban public transportation system with time delay. By combining graph theory and the Lyapunov method, the global Lyapunov function is constructed, and the response system can synchronize with the drive system under the adaptive controller.
FRACTAL AND FRACTIONAL
(2023)
Article
Chemistry, Multidisciplinary
Erhui Shang, Huiling Chen, Miaomiao He, Xinlai Zhou, Dongju Chen
Summary: In this paper, a Turing membrane with turning structure was prepared using polybenzimidazole (PBI) as raw material and coordination induced phase inversion method. The morphology of the Turing membrane was controlled by changing the content of polymer. The nanofiltration performance and stability in organic solvents of the Turing membrane were investigated.
CHEMICAL JOURNAL OF CHINESE UNIVERSITIES-CHINESE
(2023)
Article
Engineering, Multidisciplinary
Abdelazim G. Hussien, Guoxi Liang, Huiling Chen, Haiping Lin
Summary: Many real-world complex optimization problems can easily get stuck in local optima and fail to find the optimal solution, so new techniques and methods are needed to address these challenges. Metaheuristic algorithms, such as the Sine Cosine Algorithm (SCA), have gained attention due to their efficiency and simplicity. However, SCA, like other metaheuristic algorithms, has slow convergence and may struggle in sub-optimal regions. This study proposes an enhanced version of SCA called RDSCA that utilizes random spare/replacement and double adaptive weight techniques, resulting in competitive results compared to other metaheuristic algorithms.
CMES-COMPUTER MODELING IN ENGINEERING & SCIENCES
(2023)
Article
Multidisciplinary Sciences
Shujie Guo, Heng Zhang, Yulan Chang, Jihao Zhang, Huiling Chen, Linhong Zhang
Summary: This study aims to observe the current situation of nurses' presenteeism and its relationship with patient perceptions, as well as examine its implications for nursing management. The findings indicate that nurses' presenteeism is a key factor affecting patient experience. Although patients' overall experience is positive, there is still room for improvement. Therefore, reducing presenteeism among nurses is crucial for enhancing patient experience, fostering harmonious nurse-patient relationships, and achieving a shared mission.
Article
Automation & Control Systems
Chunmei Zhang, Huiling Chen, Qin Xu, Yuli Feng, Ran Li
Summary: This article discusses a class of stochastic hybrid delayed coupled systems with multiple weights, and derives several conditions for asymptotic synchronization and topology identification of the systems based on Kirchhoff's Matrix-Tree Theorem and Lyapunov stability theory.
NONLINEAR ANALYSIS-HYBRID SYSTEMS
(2024)
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)