AI Medical Compendium Topic:
Breast Neoplasms

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Stand-Alone Use of Artificial Intelligence for Digital Mammography and Digital Breast Tomosynthesis Screening: A Retrospective Evaluation.

Radiology
Background Use of artificial intelligence (AI) as a stand-alone reader for digital mammography (DM) or digital breast tomosynthesis (DBT) breast screening could ease radiologists' workload while maintaining quality. Purpose To retrospectively evaluat...

A machine and human reader study on AI diagnosis model safety under attacks of adversarial images.

Nature communications
While active efforts are advancing medical artificial intelligence (AI) model development and clinical translation, safety issues of the AI models emerge, but little research has been done. We perform a study to investigate the behaviors of an AI dia...

Unsupervised Learning Framework With Multidimensional Scaling in Predicting Epithelial-Mesenchymal Transitions.

IEEE/ACM transactions on computational biology and bioinformatics
Clustering tumor metastasis samples from gene expression data at the whole genome level remains an arduous challenge, in particular, when the number of experimental samples is small and the number of genes is huge. We focus on the prediction of the e...

Lesion Segmentation in Ultrasound Using Semi-Pixel-Wise Cycle Generative Adversarial Nets.

IEEE/ACM transactions on computational biology and bioinformatics
Breast cancer is the most common invasive cancer with the highest cancer occurrence in females. Handheld ultrasound is one of the most efficient ways to identify and diagnose the breast cancer. The area and the shape information of a lesion is very h...

MRI Radiomics of Breast Cancer: Machine Learning-Based Prediction of Lymphovascular Invasion Status.

Academic radiology
RATIONALE AND OBJECTIVES: In patients with breast cancer (BC), lymphovascular invasion (LVI) status is considered an important prognostic factor. We aimed to develop machine learning (ML)-based radiomics models for the prediction of LVI status in pat...

BreaCNet: A high-accuracy breast thermogram classifier based on mobile convolutional neural network.

Mathematical biosciences and engineering : MBE
The presence of a well-trained, mobile CNN model with a high accuracy rate is imperative to build a mobile-based early breast cancer detector. In this study, we propose a mobile neural network model breast cancer mobile network (BreaCNet) and its imp...

Direct left-ventricular global longitudinal strain (GLS) computation with a fully convolutional network.

Journal of biomechanics
This study's purpose was to develop a direct MRI-based, deep-learning semantic segmentation approach for computing global longitudinal strain (GLS), a known metric for detecting left-ventricular (LV) cardiotoxicity in breast cancer. Displacement Enco...

Artificial intelligence modelling in differentiating core biopsies of fibroadenoma from phyllodes tumor.

Laboratory investigation; a journal of technical methods and pathology
Breast fibroepithelial lesions (FEL) are biphasic tumors which consist of benign fibroadenomas (FAs) and the rarer phyllodes tumors (PTs). FAs and PTs have overlapping features, but have different clinical management, which makes correct core biopsy ...

Analysis of Influencing Factors on Hospitalization Expenses of Patients with Breast Malignant Tumor Undergoing Surgery: Based on the Neural Network and Support Vector Machine.

Journal of healthcare engineering
OBJECTIVE: Analyze the influencing factors of hospitalization expenses of breast cancer patients in a tertiary hospital in Chengdu and provide a basis and suggestion for controlling the unreasonable increase of medical expenses.