Enhancing Fetal Plane Classification Accuracy With Data Augmentation Using Diffusion Models

IF 2 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Yueying Tian, Elif Ucurum, Xudong Han, Rupert Young, Chris Chatwin, Philip Birch
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引用次数: 0

Abstract

Ultrasound imaging is widely used in medical diagnosis, especially for fetal health assessment. However, the availability of high-quality annotated ultrasound images is limited, which restricts the training of machine learning models. In this paper, we investigate the use of diffusion models to generate synthetic ultrasound images to improve the performance on fetal plane classification. We train different classifiers first on synthetic images and then fine-tune them with real images. Extensive experimental results demonstrate that incorporating generated images into training pipelines leads to better classification accuracy than training with real images alone. The findings suggest that generating synthetic data using diffusion models can be a valuable tool in overcoming the challenges of data scarcity in ultrasound medical imaging.

Abstract Image

利用扩散模型增强数据,提高胎儿平面分类精度
超声成像在医学诊断中有着广泛的应用,尤其是在胎儿健康评估中。然而,高质量的带注释超声图像的可用性是有限的,这限制了机器学习模型的训练。在本文中,我们研究了使用扩散模型来生成合成超声图像,以提高胎儿平面分类的性能。我们首先在合成图像上训练不同的分类器,然后用真实图像对它们进行微调。大量的实验结果表明,将生成的图像合并到训练管道中比单独使用真实图像进行训练具有更好的分类精度。研究结果表明,使用扩散模型生成合成数据可以成为克服超声医学成像中数据稀缺挑战的有价值的工具。
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来源期刊
IET Image Processing
IET Image Processing 工程技术-工程:电子与电气
CiteScore
5.40
自引率
8.70%
发文量
282
审稿时长
6 months
期刊介绍: The IET Image Processing journal encompasses research areas related to the generation, processing and communication of visual information. The focus of the journal is the coverage of the latest research results in image and video processing, including image generation and display, enhancement and restoration, segmentation, colour and texture analysis, coding and communication, implementations and architectures as well as innovative applications. Principal topics include: Generation and Display - Imaging sensors and acquisition systems, illumination, sampling and scanning, quantization, colour reproduction, image rendering, display and printing systems, evaluation of image quality. Processing and Analysis - Image enhancement, restoration, segmentation, registration, multispectral, colour and texture processing, multiresolution processing and wavelets, morphological operations, stereoscopic and 3-D processing, motion detection and estimation, video and image sequence processing. Implementations and Architectures - Image and video processing hardware and software, design and construction, architectures and software, neural, adaptive, and fuzzy processing. Coding and Transmission - Image and video compression and coding, compression standards, noise modelling, visual information networks, streamed video. Retrieval and Multimedia - Storage of images and video, database design, image retrieval, video annotation and editing, mixed media incorporating visual information, multimedia systems and applications, image and video watermarking, steganography. Applications - Innovative application of image and video processing technologies to any field, including life sciences, earth sciences, astronomy, document processing and security. Current Special Issue Call for Papers: Evolutionary Computation for Image Processing - https://digital-library.theiet.org/files/IET_IPR_CFP_EC.pdf AI-Powered 3D Vision - https://digital-library.theiet.org/files/IET_IPR_CFP_AIPV.pdf Multidisciplinary advancement of Imaging Technologies: From Medical Diagnostics and Genomics to Cognitive Machine Vision, and Artificial Intelligence - https://digital-library.theiet.org/files/IET_IPR_CFP_IST.pdf Deep Learning for 3D Reconstruction - https://digital-library.theiet.org/files/IET_IPR_CFP_DLR.pdf
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