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Li W, Gou F, Wu J. Artificial intelligence auxiliary diagnosis and treatment system for breast cancer in developing countries. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2024; 32:395-413. [PMID: 38189731 DOI: 10.3233/xst-230194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
BACKGROUND In many developing countries, a significant number of breast cancer patients are unable to receive timely treatment due to a large population base, high patient numbers, and limited medical resources. OBJECTIVE This paper proposes a breast cancer assisted diagnosis system based on electronic medical records. The goal of this system is to address the limitations of existing systems, which primarily rely on structured electronic records and may miss crucial information stored in unstructured records. METHODS The proposed approach is a breast cancer assisted diagnosis system based on electronic medical records. The system utilizes breast cancer enhanced convolutional neural networks with semantic initialization filters (BC-INIT-CNN). It extracts highly relevant tumor markers from unstructured medical records to aid in breast cancer staging diagnosis and effectively utilizes the important information present in unstructured records. RESULTS The model's performance is assessed using various evaluation metrics. Such as accuracy, ROC curves, and Precision-Recall curves. Comparative analysis demonstrates that the BC-INIT-CNN model outperforms several existing methods in terms of accuracy and computational efficiency. CONCLUSIONS The proposed breast cancer assisted diagnosis system based on BC-INIT-CNN showcases the potential to address the challenges faced by developing countries in providing timely treatment to breast cancer patients. By leveraging unstructured medical records and extracting relevant tumor markers, the system enables accurate staging diagnosis and enhances the utilization of valuable information.
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Affiliation(s)
- Wenxiu Li
- State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang, China
| | - Fangfang Gou
- State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang, China
| | - Jia Wu
- State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang, China
- Research Center for Artificial Intelligence, Monash University, Melbourne, Clayton VIC, Australia
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Zhan X, Long H, Gou F, Wu J. A semantic fidelity interpretable-assisted decision model for lung nodule classification. Int J Comput Assist Radiol Surg 2023:10.1007/s11548-023-03043-5. [PMID: 38141069 DOI: 10.1007/s11548-023-03043-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 11/24/2023] [Indexed: 12/24/2023]
Abstract
PURPOSE Early diagnosis of lung nodules is important for the treatment of lung cancer patients, existing capsule network-based assisted diagnostic models for lung nodule classification have shown promising prospects in terms of interpretability. However, these models lack the ability to draw features robustly at shallow networks, which in turn limits the performance of the models. Therefore, we propose a semantic fidelity capsule encoding and interpretable (SFCEI)-assisted decision model for lung nodule multi-class classification. METHODS First, we propose multilevel receptive field feature encoding block to capture multi-scale features of lung nodules of different sizes. Second, we embed multilevel receptive field feature encoding blocks in the residual code-and-decode attention layer to extract fine-grained context features. Integrating multi-scale features and contextual features to form semantic fidelity lung nodule attribute capsule representations, which consequently enhances the performance of the model. RESULTS We implemented comprehensive experiments on the dataset (LIDC-IDRI) to validate the superiority of the model. The stratified fivefold cross-validation results show that the accuracy (94.17%) of our method exceeds existing advanced approaches in the multi-class classification of malignancy scores for lung nodules. CONCLUSION The experiments confirm that the methodology proposed can effectively capture the multi-scale features and contextual features of lung nodules. It enhances the capability of shallow structure drawing features in capsule networks, which in turn improves the classification performance of malignancy scores. The interpretable model can support the physicians' confidence in clinical decision-making.
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Affiliation(s)
- Xiangbing Zhan
- State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang, 550025, China
| | - Huiyun Long
- State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang, 550025, China.
| | - Fangfang Gou
- State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang, 550025, China.
| | - Jia Wu
- State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang, 550025, China.
- Research Center for Artificial Intelligence, Monash University, Melbourne, Clayton, VIC, 3800, Australia.
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Bui PN, Le DT, Bum J, Kim S, Song SJ, Choo H. Multi-Scale Learning with Sparse Residual Network for Explainable Multi-Disease Diagnosis in OCT Images. Bioengineering (Basel) 2023; 10:1249. [PMID: 38002373 PMCID: PMC10669434 DOI: 10.3390/bioengineering10111249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Revised: 10/19/2023] [Accepted: 10/23/2023] [Indexed: 11/26/2023] Open
Abstract
In recent decades, medical imaging techniques have revolutionized the field of disease diagnosis, enabling healthcare professionals to noninvasively observe the internal structures of the human body. Among these techniques, optical coherence tomography (OCT) has emerged as a powerful and versatile tool that allows high-resolution, non-invasive, and real-time imaging of biological tissues. Deep learning algorithms have been successfully employed to detect and classify various retinal diseases in OCT images, enabling early diagnosis and treatment planning. However, existing deep learning algorithms are primarily designed for single-disease diagnosis, which limits their practical application in clinical settings where OCT images often contain symptoms of multiple diseases. In this paper, we propose an effective approach for multi-disease diagnosis in OCT images using a multi-scale learning (MSL) method and a sparse residual network (SRN). Specifically, the MSL method extracts and fuses useful features from images of different sizes to enhance the discriminative capability of a classifier and make the disease predictions interpretable. The SRN is a minimal residual network, where convolutional layers with large kernel sizes are replaced with multiple convolutional layers that have smaller kernel sizes, thereby reducing model complexity while achieving a performance similar to that of existing convolutional neural networks. The proposed multi-scale sparse residual network significantly outperforms existing methods, exhibiting 97.40% accuracy, 95.38% sensitivity, and 98.25% specificity. Experimental results show the potential of our method to improve explainable diagnosis systems for various eye diseases via visual discrimination.
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Affiliation(s)
- Phuoc-Nguyen Bui
- Department of AI Systems Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea;
| | - Duc-Tai Le
- College of Computing and Informatics, Sungkyunkwan University, Suwon 16419, Republic of Korea;
| | - Junghyun Bum
- Sungkyun AI Research Institute, Sungkyunkwan University, Suwon 16419, Republic of Korea;
| | - Seongho Kim
- Department of Ophthalmology, Kangbuk Samsung Hospital, School of Medicine, Sungkyunkwan University, Seoul 03181, Republic of Korea;
| | - Su Jeong Song
- Department of Ophthalmology, Kangbuk Samsung Hospital, School of Medicine, Sungkyunkwan University, Seoul 03181, Republic of Korea;
- Biomedical Institute for Convergence, Sungkyunkwan University, Suwon 16419, Republic of Korea
| | - Hyunseung Choo
- Department of AI Systems Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea;
- College of Computing and Informatics, Sungkyunkwan University, Suwon 16419, Republic of Korea;
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea
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He Z, Liu J, Gou F, Wu J. An Innovative Solution Based on TSCA-ViT for Osteosarcoma Diagnosis in Resource-Limited Settings. Biomedicines 2023; 11:2740. [PMID: 37893113 PMCID: PMC10604772 DOI: 10.3390/biomedicines11102740] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 09/24/2023] [Accepted: 10/08/2023] [Indexed: 10/29/2023] Open
Abstract
Identifying and managing osteosarcoma pose significant challenges, especially in resource-constrained developing nations. Advanced diagnostic methods involve isolating the nucleus from cancer cells for comprehensive analysis. However, two main challenges persist: mitigating image noise during the capture and transmission of cellular sections, and providing an efficient, accurate, and cost-effective solution for cell nucleus segmentation. To tackle these issues, we introduce the Twin-Self and Cross-Attention Vision Transformer (TSCA-ViT). This pioneering AI-based system employs a directed filtering algorithm for noise reduction and features an innovative transformer architecture with a twin attention mechanism for effective segmentation. The model also incorporates cross-attention-enabled skip connections to augment spatial information. We evaluated our method on a dataset of 1000 osteosarcoma pathology slide images from the Second People's Hospital of Huaihua, achieving a remarkable average precision of 97.7%. This performance surpasses traditional methodologies. Furthermore, TSCA-ViT offers enhanced computational efficiency owing to its fewer parameters, which results in reduced time and equipment costs. These findings underscore the superior efficacy and efficiency of TSCA-ViT, offering a promising approach for addressing the ongoing challenges in osteosarcoma diagnosis and treatment, particularly in settings with limited resources.
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Affiliation(s)
- Zengxiao He
- School of Computer Science and Engineering, Central South University, Changsha 410083, China;
| | - Jun Liu
- The Second People’s Hospital of Huaihua, Huaihua 418000, China
| | - Fangfang Gou
- State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang 550025, China;
| | - Jia Wu
- School of Computer Science and Engineering, Central South University, Changsha 410083, China;
- State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang 550025, China;
- Research Center for Artificial Intelligence, Monash University, Melbourne, Clayton, VIC 3800, Australia
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Zhan X, Liu J, Long H, Zhu J, Tang H, Gou F, Wu J. An Intelligent Auxiliary Framework for Bone Malignant Tumor Lesion Segmentation in Medical Image Analysis. Diagnostics (Basel) 2023; 13:diagnostics13020223. [PMID: 36673032 PMCID: PMC9858155 DOI: 10.3390/diagnostics13020223] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Revised: 12/17/2022] [Accepted: 01/04/2023] [Indexed: 01/11/2023] Open
Abstract
Bone malignant tumors are metastatic and aggressive, with poor treatment outcomes and prognosis. Rapid and accurate diagnosis is crucial for limb salvage and increasing the survival rate. There is a lack of research on deep learning to segment bone malignant tumor lesions in medical images with complex backgrounds and blurred boundaries. Therefore, we propose a new intelligent auxiliary framework for the medical image segmentation of bone malignant tumor lesions, which consists of a supervised edge-attention guidance segmentation network (SEAGNET). We design a boundary key points selection module to supervise the learning of edge attention in the model to retain fine-grained edge feature information. We precisely locate malignant tumors by instance segmentation networks while extracting feature maps of tumor lesions in medical images. The rich contextual-dependent information in the feature map is captured by mixed attention to better understand the uncertainty and ambiguity of the boundary, and edge attention learning is used to guide the segmentation network to focus on the fuzzy boundary of the tumor region. We implement extensive experiments on real-world medical data to validate our model. It validates the superiority of our method over the latest segmentation methods, achieving the best performance in terms of the Dice similarity coefficient (0.967), precision (0.968), and accuracy (0.996). The results prove the important contribution of the framework in assisting doctors to improve the accuracy of diagnosis and clinical efficiency.
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Affiliation(s)
- Xiangbing Zhan
- State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang 550025, China
| | - Jun Liu
- The Second People’s Hospital of Huaihua, Huaihua 418000, China
- Correspondence: (J.L.); (H.L.); (J.W.)
| | - Huiyun Long
- State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang 550025, China
- Correspondence: (J.L.); (H.L.); (J.W.)
| | - Jun Zhu
- The First People’s Hospital of Huaihua, Huaihua 418000, China
- Collaborative Innovation Center for Medical Artificial Intelligence and Big Data Decision Making Assistance, Hunan University of Medicine, Huaihua 418000, China
| | - Haoyu Tang
- The First People’s Hospital of Huaihua, Huaihua 418000, China
| | - Fangfang Gou
- State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang 550025, China
- The First People’s Hospital of Huaihua, Huaihua 418000, China
- Collaborative Innovation Center for Medical Artificial Intelligence and Big Data Decision Making Assistance, Hunan University of Medicine, Huaihua 418000, China
| | - Jia Wu
- State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang 550025, China
- The First People’s Hospital of Huaihua, Huaihua 418000, China
- Collaborative Innovation Center for Medical Artificial Intelligence and Big Data Decision Making Assistance, Hunan University of Medicine, Huaihua 418000, China
- Research Center for Artificial Intelligence, Monash University, Melbourne, VIC 3800, Australia
- Correspondence: (J.L.); (H.L.); (J.W.)
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Tang H, Huang H, Liu J, Zhu J, Gou F, Wu J. AI-Assisted Diagnosis and Decision-Making Method in Developing Countries for Osteosarcoma. Healthcare (Basel) 2022; 10:2313. [PMID: 36421636 PMCID: PMC9690527 DOI: 10.3390/healthcare10112313] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 10/28/2022] [Accepted: 11/15/2022] [Indexed: 10/29/2023] Open
Abstract
Osteosarcoma is a malignant tumor derived from primitive osteogenic mesenchymal cells, which is extremely harmful to the human body and has a high mortality rate. Early diagnosis and treatment of this disease is necessary to improve the survival rate of patients, and MRI is an effective tool for detecting osteosarcoma. However, due to the complex structure and variable location of osteosarcoma, cancer cells are highly heterogeneous and prone to aggregation and overlap, making it easy for doctors to inaccurately predict the area of the lesion. In addition, in developing countries lacking professional medical systems, doctors need to examine mass of osteosarcoma MRI images of patients, which is time-consuming and inefficient, and may result in misjudgment and omission. For the sake of reducing labor cost and improve detection efficiency, this paper proposes an Attention Condenser-based MRI image segmentation system for osteosarcoma (OMSAS), which can help physicians quickly locate the lesion area and achieve accurate segmentation of the osteosarcoma tumor region. Using the idea of AttendSeg, we constructed an Attention Condenser-based residual structure network (ACRNet), which greatly reduces the complexity of the structure and enables smaller hardware requirements while ensuring the accuracy of image segmentation. The model was tested on more than 4000 samples from two hospitals in China. The experimental results demonstrate that our model has higher efficiency, higher accuracy and lighter structure for osteosarcoma MRI image segmentation compared to other existing models.
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Affiliation(s)
- Haojun Tang
- School of Computer Science and Engineering, Central South University, Changsha 410083, China
| | - Hui Huang
- The First People’s Hospital of Huaihua, Huaihua 418000, China
| | - Jun Liu
- The Second People’s Hospital of Huaihua, Huaihua 418000, China
| | - Jun Zhu
- The First People’s Hospital of Huaihua, Huaihua 418000, China
- Collaborative Innovation Center for Medical Artificial Intelligence and Big Data Decision Making Assistance, Hunan University of Medicine, Huaihua 418000, China
| | - Fangfang Gou
- School of Computer Science and Engineering, Central South University, Changsha 410083, China
| | - Jia Wu
- School of Computer Science and Engineering, Central South University, Changsha 410083, China
- The First People’s Hospital of Huaihua, Huaihua 418000, China
- Collaborative Innovation Center for Medical Artificial Intelligence and Big Data Decision Making Assistance, Hunan University of Medicine, Huaihua 418000, China
- Research Center for Artificial Intelligence, Monash University, Melbourne, Clayton, VIC 3800, Australia
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