Self-supervised multi-modal fusion network for multi-modal thyroid ultrasound image diagnosis

Xiang, Zhuo; Zhuo, Qiuluan; Zhao, Cheng; Deng, Xiaofei; Tao, Zhu; Wang, Tianfu; Jiang, Wei; Lei, Baiying

doi:10.1016/j.compbiomed.2022.106164

Cited by 19 publications

(12 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“… 20 Our experimental results show that the performance of the deep learning model using SSL in diagnosing various types of images has been significantly improved, which is consistent with the previous research results. 21 , 22 , 23 Due to the implementation of SSL, we can also use a large number of captured unlabeled data to quickly iterate and optimize the deep learning model when mpox-AISM is used to diagnose patients with skin rashes in real-world settings. 24 To our knowledge, we adopted the SSL strategy in mpox diagnosis research for the first time and verified its effectiveness.…”

Section: Discussionmentioning

confidence: 99%

Mpox-AISM: AI-mediated super monitoring for mpox and like-mpox

Yue,

Jiang,

Zhang

et al. 2024

iScience

View full text Add to dashboard Cite

Section: Discussionmentioning

confidence: 99%

Mpox-AISM: AI-mediated super monitoring for mpox and like-mpox

Yue,

Jiang,

Zhang

et al. 2024

iScience

View full text Add to dashboard Cite

“…The multimodal fusion showcased in [23] holds signi cant potential for advancing the eld of thyroid disease diagnosis.…”

Section: Literature Review and Related Workmentioning

confidence: 99%

Multi-Layer Perceptron and Radial Basis Function for Thyroid Disease Diagnosis

Samani,

Khazaee,

Ebadollahi

2024

Preprint

View full text Add to dashboard Cite

Thyroid disease is a common health disorder that affect millions of people worldwide. Achieving an accurate diagnosis often involves conducting multiple laboratory tests. However, recent advancements in machine learning have demonstrated promising outcomes in extracting meaningful information from these tests and medical images. By leveraging machine learning techniques, healthcare professionals can enhance their ability to analyze and interpret the data obtained from these tests, leading to more accurate and efficient diagnoses of thyroid dis- eases. This paper proposes a method to enhance thyroid disease diagnosis by combining Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) neural networks. The proposed method utilizes thyroid-related features to categorize a patient’s status into hyperthyroidism, hypothyroidism, or normal. UCI Machine Learning Repository is used as the training dataset for this study. To ensure high accuracy, data is randomly fitted to both the MLP and RBF networks. Additionally, an adaptive learning rate is applied in the backpropagation training for MLP. This helps avoid local minimums and reduces loss fluctuations and divergence. On the other hand, RBF is trained using the K-means algorithm together with the RLS algorithm. Based on the results, MLP outperforms RBF when the patient’s status is A or B. However, RBF shows better detection when the patient’s status is C. Therefore, the highest accuracy is achieved when the network switches from MLP to RBF based on the patient’s status. Overall, this paper suggests that combining MLP and RBF neural networks can improve the accuracy of thyroid disease diagnosis, especially when considering different patient statuses.

show abstract

“…MRI predominates in radiogenomics for breast imaging [ 91 ] and has been found to be the most accurate test for finding BC [ 92 , 93 , 94 ]. Yamamoto et al looked at 10 patients who had preoperative dynamic contrast-enhanced (DCE)-MRI and global gene expression data [ 95 , 96 , 97 , 98 , 99 , 100 , 101 , 102 , 103 , 104 , 105 , 106 , 107 , 108 , 109 , 110 , 111 , 112 , 113 , 114 , 115 , 116 , 117 , 118 ]. The relationship between MRI phenotypes and underlying global BC gene expression patterns was presented using a preliminary radiogenomic association map.…”

Section: Current Application Of Radiogenomics In Oncologymentioning

confidence: 99%

“…Multi-modal analysis has found application across diverse domains including geographical and biomedical image analysis [ 97 , 98 ], video analysis [ 99 , 100 ], and sentiment analysis [ 101 ]. Various methods facilitate co-learning in multi-modal analysis, such as tensor learning [ 102 ], generative models [ 103 ], graphical models [ 104 , 105 ], prior knowledge regularization [ 106 ], multiple kernel learning [ 107 ], and neural networks [ 108 , 109 , 110 ].…”

Section: Current Application Of Radiogenomics In Oncologymentioning

confidence: 99%

The Convergence of Radiology and Genomics: Advancing Breast Cancer Diagnosis with Radiogenomics

Demetriou,

Lockhat,

Brzozowski

et al. 2024

Cancers

View full text Add to dashboard Cite

Despite significant progress in the prevention, screening, diagnosis, prognosis, and therapy of breast cancer (BC), it remains a highly prevalent and life-threatening disease affecting millions worldwide. Molecular subtyping of BC is crucial for predictive and prognostic purposes due to the diverse clinical behaviors observed across various types. The molecular heterogeneity of BC poses uncertainties in its impact on diagnosis, prognosis, and treatment. Numerous studies have highlighted genetic and environmental differences between patients from different geographic regions, emphasizing the need for localized research. International studies have revealed that patients with African heritage are often diagnosed at a more advanced stage and exhibit poorer responses to treatment and lower survival rates. Despite these global findings, there is a dearth of in-depth studies focusing on communities in the African region. Early diagnosis and timely treatment are paramount to improving survival rates. In this context, radiogenomics emerges as a promising field within precision medicine. By associating genetic patterns with image attributes or features, radiogenomics has the potential to significantly improve early detection, prognosis, and diagnosis. It can provide valuable insights into potential treatment options and predict the likelihood of survival, progression, and relapse. Radiogenomics allows for visual features and genetic marker linkage that promises to eliminate the need for biopsy and sequencing. The application of radiogenomics not only contributes to advancing precision oncology and individualized patient treatment but also streamlines clinical workflows. This review aims to delve into the theoretical underpinnings of radiogenomics and explore its practical applications in the diagnosis, management, and treatment of BC and to put radiogenomics on a path towards fully integrated diagnostics.

show abstract

Self-supervised multi-modal fusion network for multi-modal thyroid ultrasound image diagnosis

Cited by 19 publications

References 21 publications

Mpox-AISM: AI-mediated super monitoring for mpox and like-mpox

Mpox-AISM: AI-mediated super monitoring for mpox and like-mpox

Multi-Layer Perceptron and Radial Basis Function for Thyroid Disease Diagnosis

The Convergence of Radiology and Genomics: Advancing Breast Cancer Diagnosis with Radiogenomics

Contact Info

Product

Resources

About