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Abstract
In the rapidly evolving field of medical image analysis utilizing artificial intelligence (AI), the selection of appropriate computational models is critical for accurate diagnosis and patient care. This literature review provides a comprehensive comparison of vision transformers (ViTs) and convolutional neural networks (CNNs), the two leading techniques in the field of deep learning in medical imaging. We conducted a survey systematically. Particular attention was given to the robustness, computational efficiency, scalability, and accuracy of these models in handling complex medical datasets. The review incorporates findings from 36 studies and indicates a collective trend that transformer-based models, particularly ViTs, exhibit significant potential in diverse medical imaging tasks, showcasing superior performance when contrasted with conventional CNN models. Additionally, it is evident that pre-training is important for transformer applications. We expect this work to help researchers and practitioners select the most appropriate model for specific medical image analysis tasks, accounting for the current state of the art and future trends in the field.
Academic Publication Comparison of Vision Transformers and Convolutional Neural Networks in Medical Image Analysis: A Systematic Review
Research Abstract & Technology Focus
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A review of convolutional neural networks in computer vision
AbstractIn computer vision, a series of exemplary advances have been made in several areas involving image classification, semantic segmentation, object detection, and image super-resolution recons...
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In the realm of medical image segmentation, both CNN-based and Transformer-based models have been extensively explored. However, CNNs exhibit limitations in long-range modeling capabilities, wherea...
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Transformers Are Bayesian Networks
Transformers are the dominant architecture in AI, yet why they work remains poorly understood. This paper offers a precise answer: a transformer is a Bayesian network. We establish this in five way...
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What is the core focus of the research titled 'Comparison of Vision Transformers and Convolutional Neural Networks in Medical Image Analysis: A Systematic Review'?
This literature focuses on: Abstract In the rapidly evolving field of medical image analysis utilizing artificial intelligence (AI), the selection of appropriate computational models is critical for accurate diagnosis and patient care. This literature review provid...
Are there open-source GitHub repositories related to Comparison of Vision Transformers and Convolutional Neural Networks in Medical Image Analysis: A Systematic Review?
Yes, open-source projects like fikrikarim/parlor (On-device, real-time multimodal AI. Have natural voice and vision conversations with an AI that runs entirely on your machine. Powered by Gemma 4 E...) are actively building upon these concepts.
Which startups are commercializing the technology behind Comparison of Vision Transformers and Convolutional Neural Networks in Medical Image Analysis: A Systematic Review?
Products like Zzzappy are bringing this to market. Their focus is: Science-backed breaks to protect your vision & prevent RSI.
What other academic literature is closely related to 'Comparison of Vision Transformers and Convolutional Neural Networks in Medical Image Analysis: A Systematic Review'?
Yes, highly correlated activity was mapped. An entry titled 'A review of convolutional neural networks in computer vision' discusses this: AbstractIn computer vision, a series of exemplary advances have been made in several areas involving image classification, semantic segmentation, o...
Are there commercial applications of 'Comparison of Vision Transformers and Convolutional Neural Networks in Medical Image Analysis: A Systematic Review' in market news publications?
Yes, highly correlated activity was mapped. An entry titled 'Transformers Are Bayesian Networks' discusses this: Transformers are the dominant architecture in AI, yet why they work remains poorly understood. This paper offers a precise answer: a transformer is...
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GitHubfikrikarim/parlor
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GitHubjmerelnyc/Photo-agents
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Product HuntZzzappy
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Product HuntGLM-5V-Turbo