AIMC Topic: Breast Neoplasms

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MuDeRN: Multi-category classification of breast histopathological image using deep residual networks.

Artificial intelligence in medicine
MOTIVATION: Identifying carcinoma subtype can help to select appropriate treatment options and determining the subtype of benign lesions can be beneficial to estimate the patients' risk of developing cancer in the future. Pathologists' assessment of ...

Assessing Breast Cancer Risk with an Artificial Neural Network.

Asian Pacific journal of cancer prevention : APJCP
Objectives: Radiologists face uncertainty in making decisions based on their judgment of breast cancer risk. Artificial intelligence and machine learning techniques have been widely applied in detection/recognition of cancer. This study aimed to esta...

An Efficient Mixed-Model for Screening Differentially Expressed Genes of Breast Cancer Based on LR-RF.

IEEE/ACM transactions on computational biology and bioinformatics
To screen differentially expressed genes quickly and efficiently in breast cancer, two gene microarray datasets of breast cancer, GSE15852 and GSE45255, were downloaded from GEO. By combining the Logistic Regression and Random Forest algorithm, this ...

Identifying cytokine predictors of cognitive functioning in breast cancer survivors up to 10 years post chemotherapy using machine learning.

Journal of neuroimmunology
INTRODUCTION: The purpose of this study is to explore 13 cytokine predictors of chemotherapy-related cognitive impairment (CRCI) in breast cancer survivors (BCS) 6 months to 10 years after chemotherapy completion using a multivariate, non-parametric ...

Contralateral Breast Cancer Event Detection Using Nature Language Processing.

AMIA ... Annual Symposium proceedings. AMIA Symposium
To facilitate the identification of contralateral breast cancer events for large cohort study, we proposed and implemented a new method based on features extracted from narrative text in progress notes and features from numbers of pathology reports f...

Mass detection in digital breast tomosynthesis data using convolutional neural networks and multiple instance learning.

Computers in biology and medicine
Digital breast tomosynthesis (DBT) was developed in the field of breast cancer screening as a new tomographic technique to minimize the limitations of conventional digital mammography breast screening methods. A computer-aided detection (CAD) framewo...

Deep learning beyond cats and dogs: recent advances in diagnosing breast cancer with deep neural networks.

The British journal of radiology
Deep learning has demonstrated tremendous revolutionary changes in the computing industry and its effects in radiology and imaging sciences have begun to dramatically change screening paradigms. Specifically, these advances have influenced the develo...

Deep learning in mammography and breast histology, an overview and future trends.

Medical image analysis
Recent improvements in biomedical image analysis using deep learning based neural networks could be exploited to enhance the performance of Computer Aided Diagnosis (CAD) systems. Considering the importance of breast cancer worldwide and the promisin...

The convergence analysis of SpikeProp algorithm with smoothing L regularization.

Neural networks : the official journal of the International Neural Network Society
Unlike the first and the second generation artificial neural networks, spiking neural networks (SNNs) model the human brain by incorporating not only synaptic state but also a temporal component into their operating model. However, their intrinsic pr...

Chromosome-wide gene dosage rebalance may benefit tumor progression.

Molecular genetics and genomics : MGG
The high-risk of tumor initiation in patients with Turner syndrome (TS) characterized by X chromosome monosomy in women has been well established and aneuploidy, defined as an abnormal number of chromosomes, is a common feature in human cancer. Howev...