AIMC Topic: Breast Neoplasms

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Deep learning radiomics of ultrasonography for differentiating sclerosing adenosis from breast cancer.

Clinical hemorheology and microcirculation
OBJECTIVES: The purpose of our study is to present a method combining radiomics with deep learning and clinical data for improved differential diagnosis of sclerosing adenosis (SA)and breast cancer (BC).

New Horizons: Artificial Intelligence for Digital Breast Tomosynthesis.

Radiographics : a review publication of the Radiological Society of North America, Inc
The use of digital breast tomosynthesis (DBT) in breast cancer screening has become widely accepted, facilitating increased cancer detection and lower recall rates compared with those achieved by using full-field digital mammography (DM). However, th...

Prediction of Breast Cancer Through Random Forest.

Current medical imaging
BACKGROUND: 8% of women are diagnosed with breast cancer. (BC) BC is the second most common cause of death in both developed and undeveloped countries. BC is characterized by the mutation of genes, constant pain, changes in the size, color (redness),...

Artificial intelligence in breast cancer diagnostics.

Cell reports. Medicine
Since breast cancer deaths are mainly due to metastasis, predicting the risk that a primary tumor will develop metastasis after a first diagnosis is a central issue that could be addressed by artificial intelligence. To overcome the problem posed by ...

ICSDA: a multi-modal deep learning model to predict breast cancer recurrence and metastasis risk by integrating pathological, clinical and gene expression data.

Briefings in bioinformatics
Breast cancer patients often have recurrence and metastasis after surgery. Predicting the risk of recurrence and metastasis for a breast cancer patient is essential for the development of precision treatment. In this study, we proposed a novel multi-...

Deep learning radiomics under multimodality explore association between muscle/fat and metastasis and survival in breast cancer patients.

Briefings in bioinformatics
Sarcopenia is correlated with poor clinical outcomes in breast cancer (BC) patients. However, there is no precise quantitative study on the correlation between body composition changes and BC metastasis and survival. The present study proposed a deep...

BRACS: A Dataset for BReAst Carcinoma Subtyping in H&E Histology Images.

Database : the journal of biological databases and curation
Breast cancer is the most commonly diagnosed cancer and registers the highest number of deaths for women. Advances in diagnostic activities combined with large-scale screening policies have significantly lowered the mortality rates for breast cancer ...

Present and future of machine learning in breast surgery: systematic review.

The British journal of surgery
BACKGROUND: Machine learning is a set of models and methods that can automatically detect patterns in vast amounts of data, extract information, and use it to perform decision-making under uncertain conditions. The potential of machine learning is si...

Past, Present, and Future of Machine Learning and Artificial Intelligence for Breast Cancer Screening.

Journal of breast imaging
Breast cancer screening has evolved substantially over the past few decades because of advancements in new image acquisition systems and novel artificial intelligence (AI) algorithms. This review provides a brief overview of the history, current stat...