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Advances of Data-Driven Science in Artificial Intelligence
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Advances of Data-Driven Science in Artificial Intelligence

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".

Deadline for manuscript submissions: closed (15 April 2024) | Viewed by 16106

Special Issue Editor

Dr. Jia Wu

E-Mail Website
Guest Editor
1. Research Center for Artificial Intelligence, Monash University, Melbourne, Clayton, VIC 3800, Australia
2. School of Computer Science and Engineering, Central South University, Changsha 410083, China
Interests: medical informatics; big data research; wireless network; decision-making system; machine learning; knowledge management; computational intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Data-driven science (DDS) systems have attracted a great deal of attention in recent years. The development of DDS with medical systems in many countries may create good results. With medical resources and DDS medical systems, doctors can carry out decision analysis, including the probability of contracting an illness, which will be of great help to doctors and other decision makers. In the context of the use of decision systems in medical treatment, their features can be of use to DDS systems, hospitals, patients, and doctors, helping to create a combined communication medical system. This system would not only provide messages to patients quickly but also reduce the pressure linked to obtaining resources. In DDS medical systems, patients may receive the results of tests or be notified about the conclusions of medical examinations, leading to improved communication and reduced social contradictions between doctors and patients.

This Special Issue provides a platform for researchers from academia and industry to present their novel and unpublished work in the domain of medical imaging, decision-making systems, machine learning, and knowledge management. This will help to foster future research in emerging fields of medical artificial intelligence and its related fields.

Prof. Dr. Jia Wu
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Decision-making system
  • Machine learning
  • Computational intelligence
  • Artificial intelligence

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Published Papers (7 papers)

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Research

14 pages, 3398 KiB  
Article
An Interpretable Breast Ultrasound Image Classification Algorithm Based on Convolutional Neural Network and Transformer
by Xiangjia Meng, Jun Ma, Feng Liu, Zhihua Chen and Tingting Zhang
Mathematics 2024, 12(15), 2354; https://doi.org/10.3390/math12152354 - 28 Jul 2024
Viewed by 1018
Abstract
Breast cancer is one of the most common causes of death in women. Early signs of breast cancer can be an abnormality depicted on breast images like breast ultrasonography. Unfortunately, ultrasound images contain a lot of noise, which greatly increases the difficulty for [...] Read more.
Breast cancer is one of the most common causes of death in women. Early signs of breast cancer can be an abnormality depicted on breast images like breast ultrasonography. Unfortunately, ultrasound images contain a lot of noise, which greatly increases the difficulty for doctors to interpret them. In recent years, computer-aided diagnosis (CAD) has been widely used in medical images, reducing the workload of doctors and the probability of misdiagnosis. However, it still faces the following challenges in clinical practice: one is the lack of interpretability, and another is that the accuracy is not high enough. In this paper, we propose a classification model of breast ultrasound images that leverages tumor boundaries as prior knowledge and strengthens the model to guide classification. Furthermore, we employ the advantages of convolutional neural network (CNN) to extract local features and Transformer to extract global features to achieve information balance and complementarity between the two neural network models which increase the recognition performance of the model. Additionally, an explanation method is used to generate visual results, thereby improving the poor interpretability of deep learning models. Finally, we evaluate the model on the BUSI dataset and compare it with other CNN and Transformer models. Experimental results show that the proposed model obtains an accuracy of 0.9870 and an F1 score of 0.9872, achieving state-of-the-art performance. Full article
(This article belongs to the Special Issue Advances of Data-Driven Science in Artificial Intelligence)
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23 pages, 2907 KiB  
Article
Decision Analysis under Behavioral Economics—Incentive Mechanism for Improving Data Quality in Crowdsensing
by Jiaqi Liu, Xi Shen, Wenxi Liu, Zhi Lv, Ruoti Liu and Deng Li
Mathematics 2023, 11(10), 2288; https://doi.org/10.3390/math11102288 - 14 May 2023
Cited by 1 | Viewed by 1478
Abstract
Due to the profitability and selfishness of crowdfunding system users, under fixed budget conditions, there are problems, such as low task completion rate due to insufficient participants and low data quality. However, the existing incentive mechanisms are mainly based on traditional economics, which [...] Read more.
Due to the profitability and selfishness of crowdfunding system users, under fixed budget conditions, there are problems, such as low task completion rate due to insufficient participants and low data quality. However, the existing incentive mechanisms are mainly based on traditional economics, which believes that whether users participate in tasks depends on whether the benefits of the task outweigh the costs. Behavioral economics shows that people judge the value of gains and losses according to a reference point. The weight given to losses is more important than the weight given to the same gains. Therefore, this article considers the impact of reference dependency and loss aversion on user decision-making and proposes a participant selection mechanism based on reference dependency (PSM-RD) and a quality assurance mechanism based on loss aversion (QAM-LA). PSM-RD uses reference points to influence user pricing and selects more participants based on relative value. QAM-LA pays additional rewards based on the data quality of participants and motivates them to improve data quality by reconstructing utility functions. The simulation results show that compared with the ABSee mechanism, data quality has improved by 17%, and the value of completed tasks has increased by at least 40%. Full article
(This article belongs to the Special Issue Advances of Data-Driven Science in Artificial Intelligence)
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19 pages, 3855 KiB  
Article
A Novel Medical Decision-Making System Based on Multi-Scale Feature Enhancement for Small Samples
by Keke He, Yue Qin, Fangfang Gou and Jia Wu
Mathematics 2023, 11(9), 2116; https://doi.org/10.3390/math11092116 - 29 Apr 2023
Cited by 10 | Viewed by 1735
Abstract
The medical decision-making system is an advanced system for patients that can assist doctors in their medical work. Osteosarcoma is a primary malignant tumor of the bone, due to its specificity, such as its blurred borders, diverse tumor morphology, and inconsistent scales. Diagnosis [...] Read more.
The medical decision-making system is an advanced system for patients that can assist doctors in their medical work. Osteosarcoma is a primary malignant tumor of the bone, due to its specificity, such as its blurred borders, diverse tumor morphology, and inconsistent scales. Diagnosis is quite difficult, especially for developing countries, where medical resources are inadequate per capita and there is a lack of professionals, and the time spent in the diagnosis process may lead to a gradual deterioration of the disease. To address these, we discuss an osteosarcoma-assisted diagnosis system (OSADS) based on small samples with multi-scale feature enhancement that can assist doctors in performing preliminary automatic segmentation of osteosarcoma and reduce the workload. We proposed a multi-scale feature enhancement network (MFENet) based on few-shot learning in OSADS. Global and local feature information is extracted to effectively segment the boundaries of osteosarcoma by feeding the images into MFENet. Simultaneously, a prior mask is introduced into the network to help it maintain a certain accuracy range when segmenting different shapes and sizes, saving computational costs. In the experiments, we used 5000 osteosarcoma MRI images provided by Monash University for testing. The experiments show that our proposed method achieves 93.1% accuracy and has the highest comprehensive evaluation index compared with other methods. Full article
(This article belongs to the Special Issue Advances of Data-Driven Science in Artificial Intelligence)
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25 pages, 5161 KiB  
Article
A Tumor MRI Image Segmentation Framework Based on Class-Correlation Pattern Aggregation in Medical Decision-Making System
by Hui Wei, Baolong Lv, Feng Liu, Haojun Tang, Fangfang Gou and Jia Wu
Mathematics 2023, 11(5), 1187; https://doi.org/10.3390/math11051187 - 28 Feb 2023
Cited by 14 | Viewed by 2116
Abstract
Medical image analysis methods have been applied to clinical scenarios of tumor diagnosis and treatment. Many studies have attempted to optimize the effectiveness of tumor MRI image segmentation by deep learning, but they do not consider the optimization of local details and the [...] Read more.
Medical image analysis methods have been applied to clinical scenarios of tumor diagnosis and treatment. Many studies have attempted to optimize the effectiveness of tumor MRI image segmentation by deep learning, but they do not consider the optimization of local details and the interaction of global semantic information. Second, although medical image pattern recognition can learn representative semantic features, it is challenging to ignore useless features in order to learn generalizable embeddings. Thus, a tumor-assisted segmentation method is proposed to detect tumor lesion regions and boundaries with complex shapes. Specifically, we introduce a denoising convolutional autoencoder (DCAE) for MRI image noise reduction. Furthermore, we design a novel tumor MRI image segmentation framework (NFSR-U-Net) based on class-correlation pattern aggregation, which first aggregates class-correlation patterns in MRI images to form a class-correlational representation. Then the relationship of similar class features is identified to closely correlate the dense representations of local features for classification, which is conducive to identifying image data with high heterogeneity. Meanwhile, the model uses a spatial attention mechanism and residual structure to extract effective information of the spatial dimension and enhance statistical information in MRI images, which bridges the semantic gap in skip connections. In the study, over 4000 MRI images from the Monash University Research Center for Artificial Intelligence are analyzed. The results show that the method achieves segmentation accuracy of up to 96% for tumor MRI images with low resource consumption. Full article
(This article belongs to the Special Issue Advances of Data-Driven Science in Artificial Intelligence)
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18 pages, 2799 KiB  
Article
A Data-Driven System Based on Deep Learning for Diagnosis Fetal Cavum Septum Pellucidum in Ultrasound Images
by Yuzhou Wu, Cheng Peng, Xuechen Chen, Xin Yao and Zhigang Chen
Mathematics 2022, 10(23), 4612; https://doi.org/10.3390/math10234612 - 5 Dec 2022
Viewed by 2450
Abstract
Cavum septum pellucidum (CSP) is one of the most important physiologic structures that should be detected in Ultrasound (US) scanning for the normal development of the fetal central nervous system. However, manual measurement of CSP is still a difficult and time-consuming task due [...] Read more.
Cavum septum pellucidum (CSP) is one of the most important physiologic structures that should be detected in Ultrasound (US) scanning for the normal development of the fetal central nervous system. However, manual measurement of CSP is still a difficult and time-consuming task due to the high noise of US images, even for experienced sonographers. Especially considering that maternal mortality remains high in many developing countries, a data-driven system with a medical diagnosis can help sonographers and obstetricians make decisions rapidly and improve their work efficiency. In this study, we propose a novel data-driven system based on deep learning for the diagnosis of CSP called CA-Unet, which consists of a channel attention network to segment the CSP and a post-processing module to measure and diagnose the anomalies of CSP. We collected the US data from three hospitals in China from 2012 to 2018 year to validate the effectiveness of our system. Experiments on a fetal US dataset demonstrated that our proposed system is able to help doctors make decisions and has achieved the highest precision of 79.5% and the largest Dice score of 77.5% in the segmentation of CSP. Full article
(This article belongs to the Special Issue Advances of Data-Driven Science in Artificial Intelligence)
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21 pages, 13250 KiB  
Article
Osteosarcoma MRI Image-Assisted Segmentation System Base on Guided Aggregated Bilateral Network
by Yedong Shen, Fangfang Gou and Zhehao Dai
Mathematics 2022, 10(7), 1090; https://doi.org/10.3390/math10071090 - 28 Mar 2022
Cited by 38 | Viewed by 3483
Abstract
Osteosarcoma is a primary malignant tumor. It is difficult to cure and expensive to treat. Generally, diagnosis is made by analyzing MRI images of patients. In the process of clinical diagnosis, the mainstream method is the still time-consuming and laborious manual screening. Modern [...] Read more.
Osteosarcoma is a primary malignant tumor. It is difficult to cure and expensive to treat. Generally, diagnosis is made by analyzing MRI images of patients. In the process of clinical diagnosis, the mainstream method is the still time-consuming and laborious manual screening. Modern computer image segmentation technology can realize the automatic processing of the original image of osteosarcoma and assist doctors in diagnosis. However, to achieve a better effect of segmentation, the complexity of the model is relatively high, and the hardware conditions in developing countries are limited, so it is difficult to use it directly. Based on this situation, we propose an osteosarcoma aided segmentation method based on a guided aggregated bilateral network (OSGABN), which improves the segmentation accuracy of the model and greatly reduces the parameter scale, effectively alleviating the above problems. The fast bilateral segmentation network (FaBiNet) is used to segment images. It is a high-precision model with a detail branch that captures low-level information and a lightweight semantic branch that captures high-level semantic context. We used more than 80,000 osteosarcoma MRI images from three hospitals in China for detection, and the results showed that our model can achieve an accuracy of around 0.95 and a params of 2.33 M. Full article
(This article belongs to the Special Issue Advances of Data-Driven Science in Artificial Intelligence)
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14 pages, 2942 KiB  
Article
Multiscale Balanced-Attention Interactive Network for Salient Object Detection
by Haiyan Yang, Rui Chen and Dexiang Deng
Mathematics 2022, 10(3), 512; https://doi.org/10.3390/math10030512 - 5 Feb 2022
Cited by 3 | Viewed by 1869
Abstract
The purpose of saliency detection is to detect significant regions in the image. Great progress on salient object detection has been made using from deep-learning frameworks. How to effectively extract and integrate multiscale information with different depths is an open problem for salient [...] Read more.
The purpose of saliency detection is to detect significant regions in the image. Great progress on salient object detection has been made using from deep-learning frameworks. How to effectively extract and integrate multiscale information with different depths is an open problem for salient object detection. In this paper, we propose a processing mechanism based on a balanced attention module and interactive residual module. The mechanism addressed the acquisition of the multiscale features by capturing shallow and deep context information. For effective information fusion, a modified bi-directional propagation strategy was adopted. Finally, we used the fused multiscale information to predict saliency features, which were combined to generate the final saliency maps. The experimental results on five benchmark datasets show that the method is on a par with the state of the art for image saliency datasets, especially on the PASCAL-S datasets, where the MAE reaches 0.092, and on the DUT-OMROM datasets, where the F-measure reaches 0.763. Full article
(This article belongs to the Special Issue Advances of Data-Driven Science in Artificial Intelligence)
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