Ultrasound breast tumor detection based on vision graph neural network

Hu, Mingzhe; Wang, Jing; Chang, Chung‐Cheng; Liu, Tian; Yang, Xiaofeng

doi:10.1117/12.2654077

Cited by 3 publications

(1 citation statement)

References 21 publications

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“…The inference of such shape models is a critical first step towards a tailored simulation that permits automatic evaluation of the heart chambers' morphology and function. Hu et al [27] developed a vision graph neural networks (ViG)based pipeline that can classify breast ultrasound images.…”

Section: Related Workmentioning

confidence: 99%

Improving the Automated Diagnosis of Breast Cancer with Mesh Reconstruction of Ultrasound Images Incorporating 3D Mesh Features and a Graph Attention Network

Chowa,

Azam,

Montaha

et al. 2024

j Imaging. Inform. med.

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This study proposes a novel approach for breast tumor classification from ultrasound images into benign and malignant by converting the region of interest (ROI) of a 2D ultrasound image into a 3D representation using the point-e system, allowing for in-depth analysis of underlying characteristics. Instead of relying solely on 2D imaging features, this method extracts 3D mesh features that describe tumor patterns more precisely. Ten informative and medically relevant mesh features are extracted and assessed with two feature selection techniques. Additionally, a feature pattern analysis has been conducted to determine the feature’s significance. A feature table with dimensions of 445 × 12 is generated and a graph is constructed, considering the rows as nodes and the relationships among the nodes as edges. The Spearman correlation coefficient method is employed to identify edges between the strongly connected nodes (with a correlation score greater than or equal to 0.7), resulting in a graph containing 56,054 edges and 445 nodes. A graph attention network (GAT) is proposed for the classification task and the model is optimized with an ablation study, resulting in the highest accuracy of 99.34%. The performance of the proposed model is compared with ten machine learning (ML) models and one-dimensional convolutional neural network where the test accuracy of these models ranges from 73 to 91%. Our novel 3D mesh-based approach, coupled with the GAT, yields promising performance for breast tumor classification, outperforming traditional models, and has the potential to reduce time and effort of radiologists providing a reliable diagnostic system.

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Section: Related Workmentioning

confidence: 99%

Improving the Automated Diagnosis of Breast Cancer with Mesh Reconstruction of Ultrasound Images Incorporating 3D Mesh Features and a Graph Attention Network

Chowa,

Azam,

Montaha

et al. 2024

j Imaging. Inform. med.

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Full-dose PET synthesis from low-dose PET using 2D high efficiency denoising diffusion probabilistic model

Pan,

Abouei,

Peng

et al. 2024

Medical Imaging 2024: Clinical and Biomedical Imaging

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The purpose of this study is to reduce radiation exposure in (Positron emission tomography) PET imaging while preserving high-quality clinical PET images. We propose the PET Consistency Model (PET-CM), an efficient 2D diffusion-modelbased approach to estimate full-dose PET images from low-dose PET scans. PET-CM delivers synthetic high dose PETs of comparable quality to state-of-the-art diffusion-based methods but with significantly higher efficiency. The methodology involves adding Gaussian noise to full-dose PETs through a forward diffusion process. Subsequently, a Ushaped network (Unet) is employed for denoising in a reverse diffusion process, conditioned on corresponding low-dose PETs. Accordingly, the Unet can reverse a pure Gaussian noise into a full-dose PET corresponding to the conditioning low-dose PET. In experiments converting images of 1/8 dose to full-dose images, PET-CM achieved a normalized Mean Absolute Error (NMAE) of 1.321±0.134%, a Peak Signal-to-Noise Ratio (PSNR) of 33.587±0.674 dB, a Structural Similarity Index Measure (SSIM) of 0.960±0.008, and a Normalized Cross-Correlation (NCC) of 0.967±0.011. In scenarios converting PETs from 1/4 dose to full dose, PET-CM further showcased its capability with an NMAE of 1.123±0.112%, a PSNR of 35.851±0.871 dB, an SSIM of 0.975±0.003, and an NCC of 0.990±0.003.

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Synthetic CT generation from MRI using 3D diffusion model

Pan,

Abouei,

Wynne

et al. 2024

Medical Imaging 2024: Image Processing

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This study aims to generate high-quality synthetic Computed Tomography (sCT) from Magnetic Resonance Imaging (MRI) in order to facilitate MRI-based radiation therapy treatment planning. An MRI-to-CT Transformer-based Denoising Diffusion Probabilistic Model (CT-DDPM) is proposed to utilize a diffusion process with a V-shaped Shifted-window Transformer network (Swin-Vnet) to transform MRI into sCT. The model comprises two processes: a forward process of adding Gaussian noise to ground truth CTs to create noisy volumes, and a reverse process of denoising the noisy CT scans using the Swin-Vnet conditioned on the corresponding MRI. With an optimally trained Swin-Vnet, the reverse process generates sCTs from MRI. The method is evaluated using Mean Absolute Error (MAE) of Hounsfield unit (HU), Peak Signal-to-noise Ratio (PSNR), Multi-scale Structural Similarity Index (SSIM) and Normalized Cross-Correlation (NCC) between ground truth CTs and sCTs. Evaluated on a brain dataset, CT-DDPM demonstrated state-of-the-art quantitative results, exhibiting an MAE of 45.210±3.807 HU, a PSNR of 26.753±0.861 dB, an SSIM of 0.964±0.005, and an NCC of 0.981±0.004. Evaluated on a prostate dataset, the model also showed impressive performance with an MAE of 55.492±8.281 HU, a PSNR of 28.912±2.591 dB, an SSIM of 0.894±0.092, and an NCC of 0.945±0.054. Across both datasets, CT-DDPM significantly outperformed competing networks in most metrics, as shown in paired t-test. The source code is available at: https://github.com/shaoyanpan/Synthetic-CT-generation-from-MRI-using-3D-transformer-baseddenoising-diffusion-model

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Ultrasound breast tumor detection based on vision graph neural network

Cited by 3 publications

References 21 publications

Improving the Automated Diagnosis of Breast Cancer with Mesh Reconstruction of Ultrasound Images Incorporating 3D Mesh Features and a Graph Attention Network

Improving the Automated Diagnosis of Breast Cancer with Mesh Reconstruction of Ultrasound Images Incorporating 3D Mesh Features and a Graph Attention Network

Full-dose PET synthesis from low-dose PET using 2D high efficiency denoising diffusion probabilistic model

Synthetic CT generation from MRI using 3D diffusion model

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