Mask R-CNN assisted diagnosis of spinal tuberculosis DOI Creative Commons
Wenjun Li,

Yanfan Li,

Huan Peng

et al.

Journal of X-Ray Science and Technology, Journal Year: 2024, Volume and Issue: 33(1), P. 120 - 133

Published: Dec. 24, 2024

The prevalence of spinal tuberculosis (ST) is particularly high in underdeveloped regions with inadequate medical conditions. This not only leads to misdiagnosis and delays treatment progress but also contributes the continued transmission bacteria, posing a risk other individuals. Currently, CT imaging extensively utilized computer-aided diagnosis (CAD). main features ST on images include bone destruction, osteosclerosis, sequestration formation, intervertebral disc damage. However, manual by doctors may result subjective judgments misdiagnosis. Therefore, an accurate objective method needed for diagnosing tuberculosis. In this paper, we put forward assistive diagnostic approach that based deep learning. uses Mask R-CNN model. Moreover, modify original model network incorporating ResPath cbam* improve performance metrics, namely m A P small F1-score . Meanwhile, learning models such as Faster-RCNN SSD were compared. Experimental results demonstrate enhanced can effectively identify lesions, 0.9175, surpassing model’s 0.8340, 0.9335, outperforming 0.8657.

Language: Английский

Research on Artificial-Intelligence-Assisted Medicine: A Survey on Medical Artificial Intelligence DOI Creative Commons
Fangfang Gou, Jun Liu,

Chunwen Xiao

et al.

Diagnostics, Journal Year: 2024, Volume and Issue: 14(14), P. 1472 - 1472

Published: July 9, 2024

With the improvement of economic conditions and increase in living standards, people's attention regard to health is also continuously increasing. They are beginning place their hopes on machines, expecting artificial intelligence (AI) provide a more humanized medical environment personalized services, thus greatly expanding supply bridging gap between resource demand. development IoT technology, arrival 5G 6G communication era, enhancement computing capabilities particular, application AI-assisted healthcare have been further promoted. Currently, research field assistance deepening expanding. AI holds immense value has many potential applications institutions, patients, professionals. It ability enhance efficiency, reduce costs, improve quality intelligent service experience for professionals patients. This study elaborates history timelines field, types technologies informatics, opportunities challenges medicine. The combination profound impact human life, improving levels life changing lifestyles.

Language: Английский

Citations

28
Generative AI for synthetic data across multiple medical modalities: A systematic review of recent developments and challenges DOI Creative Commons
Mahmoud K. Ibrahim, Yasmina Al Khalil, Sina Amirrajab

et al.

Computers in Biology and Medicine, Journal Year: 2025, Volume and Issue: 189, P. 109834 - 109834

Published: March 1, 2025

This paper presents a comprehensive systematic review of generative models (GANs, VAEs, DMs, and LLMs) used to synthesize various medical data types, including imaging (dermoscopic, mammographic, ultrasound, CT, MRI, X-ray), text, time-series, tabular (EHR). Unlike previous narrowly focused reviews, our study encompasses broad array modalities explores models. Our aim is offer insights into their current future applications in research, particularly the context synthesis applications, generation techniques, evaluation methods, as well providing GitHub repository dynamic resource for ongoing collaboration innovation. search strategy queries databases such Scopus, PubMed, ArXiv, focusing on recent works from January 2021 November 2023, excluding reviews perspectives. period emphasizes advancements beyond GANs, which have been extensively covered reviews. The survey also aspect conditional generation, not similar work. Key contributions include broad, multi-modality scope that identifies cross-modality opportunities unavailable single-modality surveys. While core techniques are transferable, we find methods often lack sufficient integration patient-specific context, clinical knowledge, modality-specific requirements tailored unique characteristics data. Conditional leveraging textual conditioning multimodal remain underexplored but promising directions findings structured around three themes: (1) Synthesis highlighting clinically valid significant gaps using synthetic augmentation, validation evaluation; (2) Generation identifying personalization innovation; (3) Evaluation revealing absence standardized benchmarks, need large-scale validation, importance privacy-aware, relevant frameworks. These emphasize benchmarking comparative studies promote openness collaboration.

Language: Английский

Citations

6
Advancements in medical image segmentation: A review of transformer models DOI

S. S. Kumar

Computers & Electrical Engineering, Journal Year: 2025, Volume and Issue: 123, P. 110099 - 110099

Published: Jan. 22, 2025

Language: Английский

Citations

5
A pathology image segmentation framework based on deblurring and region proxy in medical decision-making system DOI Creative Commons
Limiao Li,

Keke He,

Xiaoyu Zhu

et al.

Biomedical Signal Processing and Control, Journal Year: 2024, Volume and Issue: 95, P. 106439 - 106439

Published: May 13, 2024

The cell nuclei in pathology images provide clinicians with important tissue information for diagnosis. However, nucleus segmentation faces various challenges such as image blurring, blurred boundaries, noise, and holes. Existing methods are difficult to achieve the expected results field of nuclei. Based on this, this study proposes a framework based deblurring region proxies (DRPVit) medical decision-making systems. First, we employ an information-enhanced iterative filtering adaptive network (IE-IFANet) improve clarity regions boundaries. Then, considering performance complexity network, RegProxy model is used segmentation. utilizes agent represent homogeneous semantics, transformer encoder relationship between these regions, finally classifier prediction. Finally, introduce combined loss function consisting boundary tversky balance optimize edges regions. In post-processing stage, eliminated holes noise predicted introduced paramedical tools measure size morphological differences. experimental evaluation show that our method outperforms comparative models improvement 3.3%, 2.3%, 2.1% Intersection over Union, Dice similarity coefficient, Recall, respectively.

Language: Английский

Citations

10
Artificial intelligence multiprocessing scheme for pathology images based on transformer for nuclei segmentation DOI Creative Commons
Fangfang Gou,

Xinrong Tang,

Jun Liu

et al.

Complex & Intelligent Systems, Journal Year: 2024, Volume and Issue: 10(4), P. 5831 - 5849

Published: May 23, 2024

Abstract Malignant tumors are a common cytopathologic disease. Pathological tissue examination is key tool for diagnosing malignant tumors. Doctors need to manually analyze the images of pathological sections, which not only time-consuming but also highly subjective, easily leading misdiagnosis. Most existing computer-aided diagnostic techniques focus too much on accuracy when processing images, and do take into account problems insufficient resources in developing countries meet training large models difficulty obtaining medical annotation data. Based this, this study proposes an artificial intelligence multiprocessing scheme (MSPInet) digital pathology We use such as data expansion noise reduction enhance dataset. Then we design coarse segmentation method cell nuclei based Transformer Semantic Segmentation further optimize tumor edges using conditional random fields. Finally, improve strategy knowledge distillation. As assistive system, can quantify convert complex analyzable image information. Experimental results show that our performs well terms has advantages time space efficiency. This makes technology available resourced, equipped care. The teacher model lightweight student included achieve 71.6% 66.1% Intersection over Union (IoU) respectively, outperforming Swin-unet CSWin Transformer.

Language: Английский

Citations

8
An intelligent MRI assisted diagnosis and treatment system for osteosarcoma based on super-resolution DOI Creative Commons
Xu Zhong, Fangfang Gou, Jia Wu

et al.

Complex & Intelligent Systems, Journal Year: 2024, Volume and Issue: 10(5), P. 6031 - 6050

Published: May 27, 2024

Abstract Magnetic resonance imaging (MRI) examinations are a routine part of the cancer treatment process. In developing countries, disease diagnosis is often time-consuming and associated with serious prognostic problems. Moreover, MRI characterized by high noise low resolution. This creates difficulties in automatic segmentation lesion region, leading to decrease performance model. paper proposes deep convolutional neural network osteosarcoma image system based on reduction super-resolution reconstruction, which first time introduce methods task segmentation, effectively improving Model generalization performance. We refined initial dataset using Differential Activation Filter, separating those data that had little effect model training. At same time, we carry out rough denoising image. Then, an improved information multi-distillation adaptive cropping proposed reconstruct original improve resolution Finally, high-resolution used segment image, boundary optimized provide reference for doctors. Experimental results show this algorithm has stronger anti-noise ability than existing methods. Code: https://github.com/GFF1228/NSRDN.

Language: Английский

Citations

7
Multi-threshold image segmentation based on an improved whale optimization algorithm: A case study of Lupus Nephritis DOI
Jinge Shi, Yi Chen, Zhennao Cai

et al.

Biomedical Signal Processing and Control, Journal Year: 2024, Volume and Issue: 96, P. 106492 - 106492

Published: June 7, 2024

Language: Английский

Citations

6
Optimization of edge server group collaboration architecture strategy in IoT smart cities application DOI
Fangfang Gou, Jia Wu

Peer-to-Peer Networking and Applications, Journal Year: 2024, Volume and Issue: 17(5), P. 3110 - 3132

Published: June 18, 2024

Language: Английский

Citations

6
Pathological Image Segmentation Method Based on Multiscale and Dual Attention DOI Creative Commons
Jia Wu, Yuxia Niu, Ziqiang Ling

et al.

International Journal of Intelligent Systems, Journal Year: 2024, Volume and Issue: 2024(1)

Published: Jan. 1, 2024

Medical images play a significant part in biomedical diagnosis, but they have feature. The medical images, influenced by factors such as imaging equipment limitations, local volume effect, and others, inevitably exhibit issues like noise, blurred edges, inconsistent signal strength. These imperfections pose challenges create obstacles for doctors during their diagnostic processes. To address these issues, we present pathology image segmentation technique based on the multiscale dual attention mechanism (MSDAUnet), which consists of three primary components. Firstly, an denoising enhancement module is constructed using dynamic residual color histogram to remove noise improve clarity. Then, propose (DAM), extracts messages from both channel spatial dimensions, obtains key features, makes edge lesion area clearer. Finally, capturing information process addresses issue uneven strength certain extent. Each combined automatic pathological segmentation. Compared with traditional typical U‐Net model, MSDAUnet has better performance. On dataset provided Research Center Artificial Intelligence Monash University, IOU index high 72.7%, nearly 7% higher than that U‐Net, DSC 84.9%, also about U‐Net.

Language: Английский

Citations

3
The relative contrast ratio of malignant bone lesion is highest on relative Fat Fraction maps compared with T1-weighted and b900 DWI images DOI
Francesca Castagnoli, Alina Dragan, Antonio Candito

et al.

Magnetic Resonance Imaging, Journal Year: 2025, Volume and Issue: unknown, P. 110331 - 110331

Published: Jan. 1, 2025

Language: Английский

Citations

0