Shear Wave Elastography-Assisted Ultrasound Breast Image Analysis and Identification of Abnormal Data

Yan, Caoxin; Luo, Zhi-yan; Lin, Zimei; He, Huilin; Luo, Yunkai; Chen, Jian

doi:10.1155/2022/5499354

Cited by 5 publications

(1 citation statement)

References 20 publications

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“… BreakHis [25]: BreakHis dataset has computed tomography images of benign and malignant breast cancer at different magnification factors (40×, 100×, 200× and 400×). In addition, the dataset has the characteristic of category imbalance, including 2480 benign and 5429 malignant samples.…”

Section: Datasetmentioning

confidence: 99%

Simplified Inception Module Based Hadamard Attention Mechanism for Medical Image Classification

Jin¹,

Zhang²,

Cai³

2023

JCC

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Medical image classification has played an important role in the medical field, and the related method based on deep learning has become an important and powerful technique in medical image classification. In this article, we propose a simplified inception module based Hadamard attention (SI + HA) mechanism for medical image classification. Specifically, we propose a new attention mechanism: Hadamard attention mechanism. It improves the accuracy of medical image classification without greatly increasing the complexity of the model. Meanwhile, we adopt a simplified inception module to improve the utilization of parameters. We use two medical image datasets to prove the superiority of our proposed method. In the BreakHis dataset, the AUCs of our method can reach 98.74%, 98.38%, 98.61% and 97.67% under the magnification factors of 40×, 100×, 200× and 400×, respectively. The accuracies can reach 95.67%, 94.17%, 94.53% and 94.12% under the magnification factors of 40×, 100×, 200× and 400×, respectively. In the KIMIA Path 960 dataset, the AUCs and accuracy of our method can reach 99.91% and 99.03%. It is superior to the currently popular methods and can significantly improve the effectiveness of medical image classification.

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Section: Datasetmentioning

confidence: 99%

Simplified Inception Module Based Hadamard Attention Mechanism for Medical Image Classification

Jin¹,

Zhang²,

Cai³

2023

JCC

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Construction of an Artificial Neural Network Model for Predicting Ankle Ligament Injury Based on the Results of Ultrasonic Shear Wave Technology and Magnetic Resonance Imaging

Zhang,

Zhao,

Zheng

et al. 2023

j biomed nanotechnol

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We aimed to study the factors influencing the extent of anterior talofibular ligament (ATFL) and calcaneal ligament (CFL) injuries in acute ankle fractures based on construction of an Artificial Neural Network (ANN) Model. Differences in various baseline data, including personal data, anthropometric data, disease history, and life history, were compared among patients with ATFL injury, ATFL+CFL injury, and ATFL fracture. Factors influencing the ATFL injury/ATFL+CFL injury/ATFL fracture were analyzed using logistic regression, and an artificial neural network (ANN) model for predicting ATFL fracture was constructed using the tensor flow framework. Advanced age (OR= 36.33, 95%CI (15.72, 60.62)), male (OR = 21.21, 95%CI (5, 39.92)), high BMI (OR = 0.03, 95%CI (−0.31 0.37)), exercise duration (OR = 0.48, 95%CI (−14.66, 18.3)), and history of diabetes (OR = 16.98, 95%CI (−76.44, 480.78)) may all be influential factors in the ATFL and CFL injury/ATFL rupture. We constructed three neural layers, the first containing 11 ganglia, the second containing 7 ganglia, and the third containing 5 ganglia, and after 10 iterations the ANN model LOSS values were reduced to the lowest and scatter plots were made of the true and predicted values with some linear trend and better prediction. Advanced age, male, high BMI, length of exercise, and history of diabetes may be influential factors in the ATFL and CFL injuries/ATFL rupture. Applying the tensor flow framework, the ANN algorithm was constructed to predict the occurrence of ATFL fracture with good results.

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Silencing the lncRNA EBLN3P Improves Prognosis in Patients with Invasive Breast Cancer by Directly Targeting miR-144-3p

Hu,

Zhang,

et al. 2025

J Environ Pathol Toxicol Oncol

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The incidence of breast cancer, a malignant tumor that causes more harm to women, is increasing year by year, affecting women at a younger age. This paper describes the possible practical significance of lncRNA EBLN3P (EBLN3P) in predicting the prognosis of invasive breast cancer. EBLN3P and miR-144-3p levels in tissues and cells were detected by real-time quantitative PCR (RT-qPCR). The association between EBLN3P expression and prognosis of invasive breast cancer was investigated using Cox multivariate regression and Kaplan-Meier curve. The growth efficacy of EBLN3P expression on invasive breast cancer cells was evaluated by Cell Counting kit-8 (CCK-8) method and Transwell method, and the mechanism of EBLN3P targeting miR-144-3p was further studied. EBLN3P was elevated in invasive breast cancer, whereas survival rates were lower in patients with high EBLN3P level. EBLN3P directly targeted miR-144-3p to participate in the mechanism of invasive breast cancer, and EBLN3P knockdown had an inhibitory effect on tumor cells. Silencing EBLN3P inhibited the advancement of invasive breast cancer and was expected to be a promising therapeutic target for clinical intervention and prognosis.

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Shear Wave Elastography-Assisted Ultrasound Breast Image Analysis and Identification of Abnormal Data

Cited by 5 publications

References 20 publications

Simplified Inception Module Based Hadamard Attention Mechanism for Medical Image Classification

Simplified Inception Module Based Hadamard Attention Mechanism for Medical Image Classification

Construction of an Artificial Neural Network Model for Predicting Ankle Ligament Injury Based on the Results of Ultrasonic Shear Wave Technology and Magnetic Resonance Imaging

Silencing the lncRNA EBLN3P Improves Prognosis in Patients with Invasive Breast Cancer by Directly Targeting miR-144-3p

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