AIMC Topic: Breast Neoplasms

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Robot-assisted Nipple-sparing Mastectomy with Immediate Breast Reconstruction: An Initial Experience.

Scientific reports
Seeking smaller and indistinct incisions, physicians have attempted endoscopic breast surgery in breast cancer patients. Unfortunately, there are some limitations in the range of movement and visualization of the operation field. Potentially addressi...

Breast cancer histopathology image classification through assembling multiple compact CNNs.

BMC medical informatics and decision making
BACKGROUND: Breast cancer causes hundreds of thousands of deaths each year worldwide. The early stage diagnosis and treatment can significantly reduce the mortality rate. However, the traditional manual diagnosis needs intense workload, and diagnosti...

Deep learning analysis of breast MRIs for prediction of occult invasive disease in ductal carcinoma in situ.

Computers in biology and medicine
PURPOSE: To determine whether deep learning-based algorithms applied to breast MR images can aid in the prediction of occult invasive disease following the diagnosis of ductal carcinoma in situ (DCIS) by core needle biopsy.

Prediction of Breast Cancer from Imbalance Respect Using Cluster-Based Undersampling Method.

Journal of healthcare engineering
To overcome the two-class imbalanced problem existing in the diagnosis of breast cancer, a hybrid of K-means and Boosted C5.0 (K-Boosted C5.0) is proposed which is based on undersampling. K-means is utilized to select the informative samples near the...

The ethical, legal and social implications of using artificial intelligence systems in breast cancer care.

Breast (Edinburgh, Scotland)
Breast cancer care is a leading area for development of artificial intelligence (AI), with applications including screening and diagnosis, risk calculation, prognostication and clinical decision-support, management planning, and precision medicine. W...

Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening.

IEEE transactions on medical imaging
We present a deep convolutional neural network for breast cancer screening exam classification, trained, and evaluated on over 200000 exams (over 1000000 images). Our network achieves an AUC of 0.895 in predicting the presence of cancer in the breast...

GSIAR: gene-subcategory interaction-based improved deep representation learning for breast cancer subcategorical analysis using gene expression, applicable for precision medicine.

Medical & biological engineering & computing
Tumor subclass detection and diagnosis is inevitable requirement for personalized medical treatment and refinement of the effects that the somatic cells show towards other clinical conditions. The genome of these somatic cells exhibits mutations and ...

Extracting comprehensive clinical information for breast cancer using deep learning methods.

International journal of medical informatics
OBJECTIVE: Breast cancer is the most common malignant tumor among women. The diagnosis and treatment information of breast cancer patients is abundant in multiple types of clinical fields, including clinicopathological data, genotype and phenotype in...

Multi-criterion mammographic risk analysis supported with multi-label fuzzy-rough feature selection.

Artificial intelligence in medicine
CONTEXT AND BACKGROUND: Breast cancer is one of the most common diseases threatening the human lives globally, requiring effective and early risk analysis for which learning classifiers supported with automated feature selection offer a potential rob...