Deep Learning Applications in Medical Image Analysis

Singha, Ananya; Thakur, R. S.; Patel, Tushar S.

doi:10.1002/9781119711278.ch11

Cited by 10 publications

(4 citation statements)

References 160 publications

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“…In conclusion, the results and discussion emphasize the positive impact of integrating machine learning into medical image analysis for artificial vision. The findings contribute valuable insights to the ongoing evolution of artificial vision systems in healthcare and underscore the potential for transformative advancements in technology [24].…”

Section: A Comprehensive Survey Of Machine Learning Applications In M...mentioning

confidence: 83%

A Comprehensive Survey of Machine Learning Applications in Medical Image Analysis for Artificial Vision

Alwiyah,

Setyowati

2023

ITALIC

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This study presents a thorough survey of the applications of machine learning in medical image analysis for artificial vision,aiming to offer a comprehensive understanding of the evolving intersection between machine learning and medical imaging.With the rapid advancement of artificial vision technologies, the integration of machine learning algorithms has becomepivotal in revolutionizing medical image analysis. The survey explores a diverse range of machine learning applicationswithin the medical imaging domain, encompassing techniques such as convolutional neural networks (CNNs), support vectormachines, and decision trees. The focus lies in elucidating the role of machine learning in enhancing the accuracy, efficiency,and diagnostic capabilities of medical image analysis systems. Key topics addressed in the survey include image segmentation, classification, and detection, with a specific emphasis on applications in radiology, pathology, and ophthalmology. Additionally, the survey discusses challenges and opportunities in the integration of machine learning into medical image analysis, providing insights into current trends and future directions. This comprehensive survey serves as a valuable resource for researchers, practitioners, and healthcare professionals seeking an in-depth overview of the diverse applications and evolving landscape of machine learning in medical image analysis for artificial vision.

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Section: A Comprehensive Survey Of Machine Learning Applications In M...mentioning

confidence: 83%

A Comprehensive Survey of Machine Learning Applications in Medical Image Analysis for Artificial Vision

Alwiyah,

Setyowati

2023

ITALIC

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“…The utilization of deep learning algorithms for image analysis has brought about a revolution in medical healthcare by facilitating advanced and automated analysis of medical images (20). Deep learning methods, including Convolutional Neural Networks (CNNs), have showcased outstanding proficiency in tasks like image segmentation, feature extraction, and classification, exhibiting remarkable performance (21). By leveraging large amounts of annotated data, deep learning models can learn intricate patterns and relationships within medical images, facilitating accurate detection, localization, and diagnosis of diseases and abnormalities.…”

Section: The Role Of Image Analysis In Medical Healthcarementioning

confidence: 99%

Medical image analysis using deep learning algorithms

Li,

Jiang,

Zhang

et al. 2023

Front. Public Health

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In the field of medical image analysis within deep learning (DL), the importance of employing advanced DL techniques cannot be overstated. DL has achieved impressive results in various areas, making it particularly noteworthy for medical image analysis in healthcare. The integration of DL with medical image analysis enables real-time analysis of vast and intricate datasets, yielding insights that significantly enhance healthcare outcomes and operational efficiency in the industry. This extensive review of existing literature conducts a thorough examination of the most recent deep learning (DL) approaches designed to address the difficulties faced in medical healthcare, particularly focusing on the use of deep learning algorithms in medical image analysis. Falling all the investigated papers into five different categories in terms of their techniques, we have assessed them according to some critical parameters. Through a systematic categorization of state-of-the-art DL techniques, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), Long Short-term Memory (LSTM) models, and hybrid models, this study explores their underlying principles, advantages, limitations, methodologies, simulation environments, and datasets. Based on our results, Python was the most frequent programming language used for implementing the proposed methods in the investigated papers. Notably, the majority of the scrutinized papers were published in 2021, underscoring the contemporaneous nature of the research. Moreover, this review accentuates the forefront advancements in DL techniques and their practical applications within the realm of medical image analysis, while simultaneously addressing the challenges that hinder the widespread implementation of DL in image analysis within the medical healthcare domains. These discerned insights serve as compelling impetuses for future studies aimed at the progressive advancement of image analysis in medical healthcare research. The evaluation metrics employed across the reviewed articles encompass a broad spectrum of features, encompassing accuracy, sensitivity, specificity, F-score, robustness, computational complexity, and generalizability.

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“…[30][31][32][33][34] To improve the interpretability of the relation between the topology of the deep model and reconstruction results, a new emerging class of deep learning-based methods known as learnable optimization algorithms (LOA) have attracted much attention e.g. [35][36][37][38][39][40][41][42][43][44][45][46][47][48][49][50][51][52][53][54] LOA was proposed to map existing optimization algorithms to structured networks where each phase of the networks correspond to one iteration of an optimization algorithm.…”

Section: Optimization-based Network Unrolling Algorithms For Mri Reco...mentioning

confidence: 99%

An Optimization-Based Meta-Learning Model for MRI Reconstruction with Diverse Dataset

Bian¹,

Chen²,

Ye³

et al. 2021

Preprint

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(1) Purpose: This work aims at developing a generalizable MRI reconstruction model in the meta-learning framework. The standard benchmarks in meta-learning are challenged by learning on diverse task distributions. The proposed network learns the regularization function in a variational model and reconstructs MR images with various under-sampling ratios or patterns that may or may not be seen in the training data by leveraging a heterogeneous dataset. (2) Methods: We propose an unrolling network induced by learnable optimization algorithms (LOA) for solving our nonconvex nonsmooth variational model for MRI reconstruction. In this model, the learnable regularization function contains a task-invariant common feature encoder and task-specific learner represented by a shallow network. To train the network we split the training data into two parts: training and validation, and introduce a bilevel optimization algorithm. The lower-level optimization trains task-invariant parameters for the feature encoder with fixed parameters of the task-specific learner on the training dataset, and the upper-level optimizes the parameters of the task-specific learner on the validation dataset. (3) Results: The PSNR increases 1.5 dB on average compared to the network trained through conventional supervised learning on the seen CS ratios. We test the result of quick adaption on the unseen tasks after meta-training, the average PSNR arises 1.22 dB compared to the conventional learning procedure that is directly trained on the unseen CS ratios in the meanwhile saving half of the training time. The average PSNR arises 1.87 dB for unseen sampling patterns comparing to conventional learning; (4) Conclusion: We proposed a meta-learning framework consisting of the base network architecture, design of regularization, and bi-level optimization-based training. The network inherits the convergence property of the LOA and interpretation of the variational model. The generalization ability is improved by the designated regularization and bilevel optimization-based training algorithm.

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Deep Learning Applications in Medical Image Analysis

Cited by 10 publications

References 160 publications

A Comprehensive Survey of Machine Learning Applications in Medical Image Analysis for Artificial Vision

A Comprehensive Survey of Machine Learning Applications in Medical Image Analysis for Artificial Vision

Medical image analysis using deep learning algorithms

An Optimization-Based Meta-Learning Model for MRI Reconstruction with Diverse Dataset

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