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Petmezas G, Papageorgiou VE, Vassilikos V, Pagourelias E, Tsaklidis G, Katsaggelos AK, Maglaveras N. Recent advancements and applications of deep learning in heart failure: Α systematic review. Comput Biol Med 2024; 176:108557. [PMID: 38728995 DOI: 10.1016/j.compbiomed.2024.108557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Revised: 04/12/2024] [Accepted: 05/05/2024] [Indexed: 05/12/2024]
Abstract
BACKGROUND Heart failure (HF), a global health challenge, requires innovative diagnostic and management approaches. The rapid evolution of deep learning (DL) in healthcare necessitates a comprehensive review to evaluate these developments and their potential to enhance HF evaluation, aligning clinical practices with technological advancements. OBJECTIVE This review aims to systematically explore the contributions of DL technologies in the assessment of HF, focusing on their potential to improve diagnostic accuracy, personalize treatment strategies, and address the impact of comorbidities. METHODS A thorough literature search was conducted across four major electronic databases: PubMed, Scopus, Web of Science and IEEE Xplore, yielding 137 articles that were subsequently categorized into five primary application areas: cardiovascular disease (CVD) classification, HF detection, image analysis, risk assessment, and other clinical analyses. The selection criteria focused on studies utilizing DL algorithms for HF assessment, not limited to HF detection but extending to any attempt in analyzing and interpreting HF-related data. RESULTS The analysis revealed a notable emphasis on CVD classification and HF detection, with DL algorithms showing significant promise in distinguishing between affected individuals and healthy subjects. Furthermore, the review highlights DL's capacity to identify underlying cardiomyopathies and other comorbidities, underscoring its utility in refining diagnostic processes and tailoring treatment plans to individual patient needs. CONCLUSIONS This review establishes DL as a key innovation in HF management, highlighting its role in advancing diagnostic accuracy and personalized care. The insights provided advocate for the integration of DL in clinical settings and suggest directions for future research to enhance patient outcomes in HF care.
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Affiliation(s)
- Georgios Petmezas
- 2nd Department of Obstetrics and Gynecology, Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece; Centre for Research and Technology Hellas, Thessaloniki, Greece.
| | | | - Vasileios Vassilikos
- 3rd Department of Cardiology, Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Efstathios Pagourelias
- 3rd Department of Cardiology, Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - George Tsaklidis
- Department of Mathematics, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Aggelos K Katsaggelos
- Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL, USA
| | - Nicos Maglaveras
- 2nd Department of Obstetrics and Gynecology, Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
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Lan K, Cheng J, Jiang J, Jiang X, Zhang Q. Modified UNet++ with atrous spatial pyramid pooling for blood cell image segmentation. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:1420-1433. [PMID: 36650817 DOI: 10.3934/mbe.2023064] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Blood cell image segmentation is an important part of the field of computer-aided diagnosis. However, due to the low contrast, large differences in cell morphology and the scarcity of labeled images, the segmentation performance of cells cannot meet the requirements of an actual diagnosis. To address the above limitations, we present a deep learning-based approach to study cell segmentation on pathological images. Specifically, the algorithm selects UNet++ as the backbone network to extract multi-scale features. Then, the skip connection is redesigned to improve the degradation problem and reduce the computational complexity. In addition, the atrous spatial pyramid pooling (ASSP) is introduced to obtain cell image information features from each layer through different receptive domains. Finally, the multi-sided output fusion (MSOF) strategy is utilized to fuse the features of different semantic levels, so as to improve the accuracy of target segmentation. Experimental results on blood cell images for segmentation and classification (BCISC) dataset show that the proposed method has significant improvement in Matthew's correlation coefficient (Mcc), Dice and Jaccard values, which are better than the classical semantic segmentation network.
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Affiliation(s)
- Kun Lan
- College of Mechanical Engineering, Quzhou University, Quzhou 324000, China
| | - Jianzhen Cheng
- Department of Rehabilitation, Quzhou Third Hospital, Quzhou 324000, China
| | - Jinyun Jiang
- College of Mechanical Engineering, Quzhou University, Quzhou 324000, China
| | - Xiaoliang Jiang
- College of Mechanical Engineering, Quzhou University, Quzhou 324000, China
| | - Qile Zhang
- Department of Rehabilitation, The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People's Hospital, Quzhou 324000, China
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DeepDA-Ace: A Novel Domain Adaptation Method for Species-Specific Acetylation Site Prediction. MATHEMATICS 2022. [DOI: 10.3390/math10142364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Protein lysine acetylation is an important type of post-translational modification (PTM), and it plays a crucial role in various cellular processes. Recently, although many researchers have focused on developing tools for acetylation site prediction based on computational methods, most of these tools are based on traditional machine learning algorithms for acetylation site prediction without species specificity, still maintained as a single prediction model. Recent studies have shown that the acetylation sites of distinct species have evident location-specific differences; however, there is currently no integrated prediction model that can effectively predict acetylation sites cross all species. Therefore, to enhance the scope of species-specific level, it is necessary to establish a framework for species-specific acetylation site prediction. In this work, we propose a domain adaptation framework DeepDA-Ace for species-specific acetylation site prediction, including Rattus norvegicus, Schistosoma japonicum, Arabidopsis thaliana, and other types of species. In DeepDA-Ace, an attention based densely connected convolutional neural network is designed to capture sequence features, and the semantic adversarial learning strategy is proposed to align features of different species so as to achieve knowledge transfer. The DeepDA-Ace outperformed both the general prediction model and fine-tuning based species-specific model across most types of species. The experiment results have demonstrated that DeepDA-Ace is superior to the general and fine-tuning methods, and its precision exceeds 0.75 on most species. In addition, our method achieves at least 5% improvement over the existing acetylation prediction tools.
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He Y, Li J, Shen S, Liu K, Wong KK, He T, Wong STC. Image-to-image translation of label-free molecular vibrational images for a histopathological review using the UNet+/seg-cGAN model. BIOMEDICAL OPTICS EXPRESS 2022; 13:1924-1938. [PMID: 35519236 PMCID: PMC9045908 DOI: 10.1364/boe.445319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Revised: 12/23/2021] [Accepted: 02/09/2022] [Indexed: 06/14/2023]
Abstract
Translating images generated by label-free microscopy imaging, such as Coherent Anti-Stokes Raman Scattering (CARS), into more familiar clinical presentations of histopathological images will help the adoption of real-time, spectrally resolved label-free imaging in clinical diagnosis. Generative adversarial networks (GAN) have made great progress in image generation and translation, but have been criticized for lacking precision. In particular, GAN has often misinterpreted image information and identified incorrect content categories during image translation of microscopy scans. To alleviate this problem, we developed a new Pix2pix GAN model that simultaneously learns classifying contents in the images from a segmentation dataset during the image translation training. Our model integrates UNet+ with seg-cGAN, conditional generative adversarial networks with partial regularization of segmentation. Technical innovations of the UNet+/seg-cGAN model include: (1) replacing UNet with UNet+ as the Pix2pix cGAN's generator to enhance pattern extraction and richness of the gradient, and (2) applying the partial regularization strategy to train a part of the generator network as the segmentation sub-model on a separate segmentation dataset, thus enabling the model to identify correct content categories during image translation. The quality of histopathological-like images generated based on label-free CARS images has been improved significantly.
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Affiliation(s)
- Yunjie He
- Translational Biophotonics Laboratory,
Systems Medicine and Bioengineering Department, Houston
Methodist Cancer Center, Houston, USA
| | - Jiasong Li
- Translational Biophotonics Laboratory,
Systems Medicine and Bioengineering Department, Houston
Methodist Cancer Center, Houston, USA
| | - Steven Shen
- Pathology and Genome Medicine Department,
Houston Methodist Hospital, Weill Cornell
Medicine, Houston, USA
| | - Kai Liu
- Translational Biophotonics Laboratory,
Systems Medicine and Bioengineering Department, Houston
Methodist Cancer Center, Houston, USA
| | - Kelvin K. Wong
- Translational Biophotonics Laboratory,
Systems Medicine and Bioengineering Department, Houston
Methodist Cancer Center, Houston, USA
- T.T. and W. F. Chao Center for BRAIN,
Houston Methodist Academic Institute,
USA
| | - Tiancheng He
- Translational Biophotonics Laboratory,
Systems Medicine and Bioengineering Department, Houston
Methodist Cancer Center, Houston, USA
| | - Stephen T. C. Wong
- Translational Biophotonics Laboratory,
Systems Medicine and Bioengineering Department, Houston
Methodist Cancer Center, Houston, USA
- Pathology and Genome Medicine Department,
Houston Methodist Hospital, Weill Cornell
Medicine, Houston, USA
- T.T. and W. F. Chao Center for BRAIN,
Houston Methodist Academic Institute,
USA
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Penso M, Solbiati S, Moccia S, Caiani EG. Decision Support Systems in HF based on Deep Learning Technologies. Curr Heart Fail Rep 2022; 19:38-51. [PMID: 35142985 PMCID: PMC9023383 DOI: 10.1007/s11897-022-00540-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 01/20/2022] [Indexed: 11/26/2022]
Abstract
Purpose of Review Application of deep learning (DL) is growing in the last years, especially in the healthcare domain. This review presents the current state of DL techniques applied to electronic health record structured data, physiological signals, and imaging modalities for the management of heart failure (HF), focusing in particular on diagnosis, prognosis, and re-hospitalization risk, to explore the level of maturity of DL in this field. Recent Findings DL allows a better integration of different data sources to distillate more accurate outcomes in HF patients, thus resulting in better performance when compared to conventional evaluation methods. While applications in image and signal processing for HF diagnosis have reached very high performance, the application of DL to electronic health records and its multisource data for prediction could still be improved, despite the already promising results. Summary Embracing the current big data era, DL can improve performance compared to conventional techniques and machine learning approaches. DL algorithms have potential to provide more efficient care and improve outcomes of HF patients, although further investigations are needed to overcome current limitations, including results generalizability and transparency and explicability of the evidences supporting the process.
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Affiliation(s)
- Marco Penso
- Department of Electronics, Information and Biomedical Engineering, Politecnico Di Milano, P.zza L. da Vinci 32, 20133, Milan, Italy
- Centro Cardiologico Monzino IRCCS, Milan, Italy
| | - Sarah Solbiati
- Department of Electronics, Information and Biomedical Engineering, Politecnico Di Milano, P.zza L. da Vinci 32, 20133, Milan, Italy
- Institute of Electronics, Information Engineering and Telecommunications (IEIIT), Italian National Research Council (CNR), Milan, Italy
| | - Sara Moccia
- The BioRobotics Institute, Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Enrico G Caiani
- Department of Electronics, Information and Biomedical Engineering, Politecnico Di Milano, P.zza L. da Vinci 32, 20133, Milan, Italy.
- Institute of Electronics, Information Engineering and Telecommunications (IEIIT), Italian National Research Council (CNR), Milan, Italy.
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DA-IMRN: Dual-Attention-Guided Interactive Multi-Scale Residual Network for Hyperspectral Image Classification. REMOTE SENSING 2022. [DOI: 10.3390/rs14030530] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
Deep learning-based fusion of spectral-spatial information is increasingly dominant for hyperspectral image (HSI) classification. However, due to insufficient samples, current feature fusion methods often neglect joint interactions. In this paper, to further improve the classification accuracy, we propose a dual-attention-guided interactive multi-scale residual network (DA-IMRN) to explore the joint spectral-spatial information and assign pixel-wise labels for HSIs without information leakage. In DA-IMRN, two branches focusing on spatial and spectral information separately are employed for feature extraction. A bidirectional-attention mechanism is employed to guide the interactive feature learning between two branches and promote refined feature maps. In addition, we extract deep multi-scale features corresponding to multiple receptive fields from limited samples via a multi-scale spectral/spatial residual block, to improve classification performance. Experimental results on three benchmark datasets (i.e., Salinas Valley, Pavia University, and Indian Pines) support that attention-guided multi-scale feature learning can effectively explore the joint spectral-spatial information. The proposed method outperforms state-of-the-art methods with the overall accuracy of 91.26%, 93.33%, and 82.38%, and the average accuracy of 94.22%, 89.61%, and 80.35%, respectively.
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A Pulmonary Vascular Extraction Algorithm from Chest CT/CTA Images. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:5763177. [PMID: 34777735 PMCID: PMC8589491 DOI: 10.1155/2021/5763177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Revised: 09/15/2021] [Accepted: 10/08/2021] [Indexed: 11/28/2022]
Abstract
Segmentation of pulmonary vessels in CT/CTA images can help physicians better determine the patient's condition and treatment. However, due to the complexity of CT images, existing methods have limitations in the segmentation of pulmonary vessels. In this paper, a method based on the separation of pulmonary vessels in CT/CTA images is investigated. The method is divided into two steps: in the first step, the lung parenchyma is extracted using the Unet++ algorithm, which can effectively reduce the oversegmentation rate; in the second step, the pulmonary vessels in the lung parenchyma are extracted using nnUnet. According to the obtained lung parenchyma segmentation results, the “AND” operation is performed on the original image and the lung parenchyma segmentation results, and only the blood vessels within the lung parenchyma are segmented, which reduces the interference of external tissues and improves the segmentation accuracy. The experimental data source used CT/CTA images acquired from the partner hospital. After the experiments were performed on a total of 67 sets of images, the accuracy of CT and CTA images reached 85.1% and 87.7%, respectively. The comparison of whether to segment the lung parenchyma and with other conventional methods was also performed, and the experimental results showed that the algorithm in this paper has high accuracy.
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CST: A Multitask Learning Framework for Colorectal Cancer Region Mining Based on Transformer. BIOMED RESEARCH INTERNATIONAL 2021; 2021:6207964. [PMID: 34671677 PMCID: PMC8523251 DOI: 10.1155/2021/6207964] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Accepted: 08/30/2021] [Indexed: 11/17/2022]
Abstract
Colorectal cancer is a high death rate cancer until now; from the clinical view, the diagnosis of the tumour region is critical for the doctors. But with data accumulation, this task takes lots of time and labor with large variances between different doctors. With the development of computer vision, detection and segmentation of the colorectal cancer region from CT or MRI image series are a great challenge in the past decades, and there still have great demands on automatic diagnosis. In this paper, we proposed a novel transfer learning protocol, called CST, that is, a union framework for colorectal cancer region detection and segmentation task based on the transformer model, which effectively constructs the cancer region detection and its segmentation jointly. To make a higher detection accuracy, we incorporate an autoencoder-based image-level decision approach that leverages the image-level decision of a cancer slice. We also compared our framework with one-stage and two-stage object detection methods; the results show that our proposed method achieves better results on detection and segmentation tasks. And this proposed framework will give another pathway for colorectal cancer screen by way of artificial intelligence.
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9
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Zou L, Liu W, Lei M, Yu X. An Improved Residual Network for Pork Freshness Detection Using Near-Infrared Spectroscopy. ENTROPY 2021; 23:e23101293. [PMID: 34682017 PMCID: PMC8534637 DOI: 10.3390/e23101293] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Revised: 09/24/2021] [Accepted: 09/28/2021] [Indexed: 12/17/2022]
Abstract
Effective and rapid assessment of pork freshness is significant for monitoring pork quality. However, a traditional sensory evaluation method is subjective and physicochemical analysis is time-consuming. In this study, the near-infrared spectroscopy (NIRS) technique, a fast and non-destructive analysis method, is employed to determine pork freshness. Considering that commonly used statistical modeling methods require preprocessing data for satisfactory performance, this paper presents a one-dimensional squeeze-and-excitation residual network (1D-SE-ResNet) to construct the complex relationship between pork freshness and NIRS. The developed model enhances the one-dimensional residual network (1D-ResNet) with squeeze-and-excitation (SE) blocks. As a deep learning model, the proposed method is capable of extracting features from the input spectra automatically and can be used as an end-to-end model to simplify the modeling process. A comparison between the proposed method and five popular classification models indicates that the 1D-SE-ResNet achieves the best performance, with a classification accuracy of 93.72%. The research demonstrates that the NIRS analysis technique based on deep learning provides a promising tool for pork freshness detection and therefore is helpful for ensuring food safety.
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Affiliation(s)
- Liang Zou
- School of Information and Electrical Control Engineering, China University of Mining and Technology, Xuzhou 221116, China; (L.Z.); (W.L.); (M.L.)
| | - Weinan Liu
- School of Information and Electrical Control Engineering, China University of Mining and Technology, Xuzhou 221116, China; (L.Z.); (W.L.); (M.L.)
| | - Meng Lei
- School of Information and Electrical Control Engineering, China University of Mining and Technology, Xuzhou 221116, China; (L.Z.); (W.L.); (M.L.)
| | - Xinhui Yu
- Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, BC V6T 1Z4, Canada
- Correspondence:
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