AIMC Topic: Breast Neoplasms

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Identifying Cancer Subtypes Using a Residual Graph Convolution Model on a Sample Similarity Network.

Genes
Cancer subtype classification helps us to understand the pathogenesis of cancer and develop new cancer drugs, treatment from which patients would benefit most. Most previous studies detect cancer subtypes by extracting features from individual sample...

Weakly-supervised deep learning for ultrasound diagnosis of breast cancer.

Scientific reports
Conventional deep learning (DL) algorithm requires full supervision of annotating the region of interest (ROI) that is laborious and often biased. We aimed to develop a weakly-supervised DL algorithm that diagnosis breast cancer at ultrasound without...

Machine Learning of Medical Applications Involving Complicated Proteins and Genetic Measurements.

Computational intelligence and neuroscience
. Breast cancer is the second greatest cause of cancer mortality among women, according to the World Health Organization (WHO), and one of the most frequent illnesses among all women today. The influence is not confined to industrialized nations but ...

MFmap: A semi-supervised generative model matching cell lines to tumours and cancer subtypes.

PloS one
Translating in vitro results from experiments with cancer cell lines to clinical applications requires the selection of appropriate cell line models. Here we present MFmap (model fidelity map), a machine learning model to simultaneously predict the c...

Optimizing the transfer-learning with pretrained deep convolutional neural networks for first stage breast tumor diagnosis using breast ultrasound visual images.

Microscopy research and technique
Female accounts for approximately 50% of the total population worldwide and many of them had breast cancer. Computer-aided diagnosis frameworks could reduce the number of needless biopsies and the workload of radiologists. This research aims to detec...

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...