A comparison review of transfer learning and self-supervised learning: Definitions, applications, advantages and limitations

Expert Systems with Applications, Journal Year: 2023, Volume and Issue: 242, P. 122807 - 122807

Published: Dec. 2, 2023

Deep learning has emerged as a powerful tool in various domains, revolutionising machine research. However, one persistent challenge is the scarcity of labelled training data, which hampers performance and generalisation deep models. To address this limitation, researchers have developed innovative methods to overcome data enhance model capabilities. Two prevalent techniques that gained significant attention are transfer self-supervised learning. Transfer leverages knowledge learned from pre-training on large-scale dataset, such ImageNet, applies it target task with limited data. This approach allows models benefit representations effectively new tasks, resulting improved generalisation. On other hand, focuses using pretext tasks do not require manual annotation, allowing them learn valuable large amounts unlabelled These can then be fine-tuned for downstream mitigating need extensive In recent years, found applications fields, including medical image processing, video recognition, natural language processing. approaches demonstrated remarkable achievements, enabling breakthroughs areas disease diagnosis, object understanding. while these offer numerous advantages, they also limitations. For example, may face domain mismatch issues between requires careful design ensure meaningful representations. review paper explores fields within past three years. It delves into advantages limitations each approach, assesses employing techniques, identifies potential directions future By providing comprehensive current methods, article offers guidance selecting best technique specific issue.

Language: Английский

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Explainable AI-driven IoMT fusion: Unravelling techniques, opportunities, and challenges with Explainable AI in healthcare

Information Fusion, Journal Year: 2024, Volume and Issue: 110, P. 102472 - 102472

Published: May 16, 2024

Language: Английский

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Artificial intelligence and multimodal data fusion for smart healthcare: topic modeling and bibliometrics

Artificial Intelligence Review, Journal Year: 2024, Volume and Issue: 57(4)

Published: March 15, 2024

Abstract Advancements in artificial intelligence (AI) have driven extensive research into developing diverse multimodal data analysis approaches for smart healthcare. There is a scarcity of large-scale literature this field based on quantitative approaches. This study performed bibliometric and topic modeling examination 683 articles from 2002 to 2022, focusing topics trends, journals, countries/regions, institutions, authors, scientific collaborations. Results showed that, firstly, the number has grown 1 220 with majority being published interdisciplinary journals that link healthcare medical information technology AI. Secondly, significant rise quantity can be attributed increasing contribution scholars non-English speaking countries/regions noteworthy contributions made by authors USA India. Thirdly, researchers show high interest issues, especially, cross-modality magnetic resonance imaging (MRI) brain tumor analysis, cancer prognosis through multi-dimensional AI-assisted diagnostics personalization healthcare, each experiencing increase interest. an emerging trend towards issues such as applying generative adversarial networks contrastive learning image fusion synthesis utilizing combined spatiotemporal resolution functional MRI electroencephalogram data-centric manner. valuable enhancing researchers’ practitioners’ understanding present focal points upcoming trajectories AI-powered analysis.

Language: Английский

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Towards Risk-Free Trustworthy Artificial Intelligence: Significance and Requirements

International Journal of Intelligent Systems, Journal Year: 2023, Volume and Issue: 2023, P. 1 - 41

Published: Oct. 26, 2023

Given the tremendous potential and influence of artificial intelligence (AI) algorithmic decision-making (DM), these systems have found wide-ranging applications across diverse fields, including education, business, healthcare industries, government, justice sectors. While AI DM offer significant benefits, they also carry risk unfavourable outcomes for users society. As a result, ensuring safety, reliability, trustworthiness becomes crucial. This article aims to provide comprehensive review synergy between DM, focussing on importance trustworthiness. The addresses following four key questions, guiding readers towards deeper understanding this topic: (i) why do we need trustworthy AI? (ii) what are requirements In line with second question, that establish been explained, explainability, accountability, robustness, fairness, acceptance AI, privacy, accuracy, reproducibility, human agency, oversight. (iii) how can data? (iv) priorities in terms challenging applications? Regarding last six different discussed, environmental science, 5G-based IoT networks, robotics architecture, engineering construction, financial technology, healthcare. emphasises address before their deployment order achieve goal good. An example is provided demonstrates be employed eliminate bias resources management systems. insights recommendations presented paper will serve as valuable guide researchers seeking applications.

Language: Английский

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Deep Learning Approaches for Medical Image Analysis and Diagnosis

Cureus, Journal Year: 2024, Volume and Issue: unknown

Published: May 2, 2024

In addition to enhancing diagnostic accuracy, deep learning techniques offer the potential streamline workflows, reduce interpretation time, and ultimately improve patient outcomes. The scalability adaptability of algorithms enable their deployment across diverse clinical settings, ranging from radiology departments point-of-care facilities. Furthermore, ongoing research efforts focus on addressing challenges data heterogeneity, model interpretability, regulatory compliance, paving way for seamless integration solutions into routine practice. As field continues evolve, collaborations between clinicians, scientists, industry stakeholders will be paramount in harnessing full advancing medical image analysis diagnosis. with other technologies, including natural language processing computer vision, may foster multimodal decision support systems care. future diagnosis is promising. With each success advancement, this technology getting closer being leveraged purposes. Beyond analysis, care pathways like imaging, imaging genomics, intelligent operating rooms or intensive units can benefit models.

Language: Английский

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Comprehensive review of deep learning in orthopaedics: Applications, challenges, trustworthiness, and fusion

Artificial Intelligence in Medicine, Journal Year: 2024, Volume and Issue: 155, P. 102935 - 102935

Published: July 26, 2024

Deep learning (DL) in orthopaedics has gained significant attention recent years. Previous studies have shown that DL can be applied to a wide variety of orthopaedic tasks, including fracture detection, bone tumour diagnosis, implant recognition, and evaluation osteoarthritis severity. The utilisation is expected increase, owing its ability present accurate diagnoses more efficiently than traditional methods many scenarios. This reduces the time cost diagnosis for patients surgeons. To our knowledge, no exclusive study comprehensively reviewed all aspects currently used practice. review addresses this knowledge gap using articles from Science Direct, Scopus, IEEE Xplore, Web between 2017 2023. authors begin with motivation orthopaedics, enhance treatment planning. then covers various applications detection supraspinatus tears MRI, osteoarthritis, prediction types arthroplasty implants, age assessment, joint-specific soft tissue disease. We also examine challenges implementing scarcity data train lack interpretability, as well possible solutions these common pitfalls. Our work highlights requirements achieve trustworthiness outcomes generated by DL, need accuracy, explainability, fairness models. pay particular fusion techniques one ways increase trustworthiness, which been address multimodality orthopaedics. Finally, we approval set forth US Food Drug Administration enable use applications. As such, aim function guide researchers develop reliable application tasks scratch market.

Language: Английский

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MEFF – A model ensemble feature fusion approach for tackling adversarial attacks in medical imaging

Intelligent Systems with Applications, Journal Year: 2024, Volume and Issue: 22, P. 200355 - 200355

Published: March 16, 2024

Adversarial attacks pose a significant threat to deep learning models, specifically medical images, as they can mislead models into making inaccurate predictions by introducing subtle distortions the input data that are often imperceptible humans. Although adversarial training is common technique used mitigate these on it lacks flexibility address new attack methods and effectively improve feature representation. This paper introduces novel Model Ensemble Feature Fusion (MEFF) designed combat in image applications. The proposed model employs fusion combining features extracted from different DL then trains Machine Learning classifiers using fused features. It uses concatenation method merge features, forming more comprehensive representation enhancing model's ability classify classes accurately. Our experimental study has performed evaluation of MEFF, considering several challenging scenarios, including 2D 3D greyscale colour binary classification, multi-label classification. reported results demonstrate robustness MEFF against types across six distinct A key advantage its capability incorporate wide range without need train scratch. Therefore, contributes developing diverse robust defense strategy. More importantly, leveraging ensemble modeling, enhances resilience face attacks, paving way for improved reliability analysis.

Language: Английский

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FracNet: An end-to-end deep learning framework for bone fracture detection

Pattern Recognition Letters, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 1, 2025

Language: Английский

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SSP: self-supervised pertaining technique for classification of shoulder implants in x-ray medical images: a broad experimental study

Artificial Intelligence Review, Journal Year: 2024, Volume and Issue: 57(10)

Published: Aug. 18, 2024

Abstract Multiple pathologic conditions can lead to a diseased and symptomatic glenohumeral joint for which total shoulder arthroplasty (TSA) replacement may be indicated. The long-term survival of implants is limited. With the increasing incidence surgery, it anticipated that revision surgery will become more common. It challenging at times retrieve manufacturer in situ implant. Therefore, certain systems facilitated by AI techniques such as deep learning (DL) help correctly identify implanted prosthesis. Correct identification reduce perioperative complications complications. DL was used this study categorise different based on X-ray images into four classes (as first case small dataset): Cofield, Depuy, Tornier, Zimmer. Imbalanced public datasets poor performance model training. Most methods literature have adopted idea transfer (TL) from ImageNet models. This type TL has been proven ineffective due some concerns regarding contrast between features learnt natural (ImageNet: colour images) (greyscale images). To address that, new approach (self-supervised pertaining (SSP)) proposed resolve issue datasets. SSP training models (ImageNet models) large number unlabelled greyscale medical domain update features. are then trained labelled data set implants. shows excellent results five models, including MobilNetV2, DarkNet19, Xception, InceptionResNetV2, EfficientNet with precision 96.69%, 95.45%, 98.76%, 98.35%, 96.6%, respectively. Furthermore, shown domains (such ImageNet) do not significantly affect images. A lightweight scratch achieves 96.6% accuracy, similar using standard extracted train several ML classifiers show outstanding obtaining an accuracy 99.20% Xception+SVM. Finally, extended experimentation carried out elucidate our approach’s real effectiveness dealing imaging scenarios. Specifically, tested without SSP, 99.47% CT brain stroke 98.60%.

Language: Английский

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Multistage transfer learning for medical images

Artificial Intelligence Review, Journal Year: 2024, Volume and Issue: 57(9)

Published: Aug. 6, 2024

Abstract Deep learning is revolutionizing various domains and significantly impacting medical image analysis. Despite notable progress, numerous challenges remain, necessitating the refinement of deep algorithms for optimal performance in This paper explores growing demand precise robust analysis by focusing on an advanced technique, multistage transfer learning. Over past decade, has emerged as a pivotal strategy, particularly overcoming associated with limited data model generalization. However, absence well-compiled literature capturing this development remains gap field. exhaustive investigation endeavors to address providing foundational understanding how approaches confront unique posed insufficient datasets. The offers detailed types, architectures, methodologies, strategies deployed Additionally, it delves into intrinsic within framework, comprehensive overview current state while outlining potential directions advancing methodologies future research. underscores transformative analysis, valuable guidance researchers healthcare professionals.

Language: Английский

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