AIMC Topic: Breast Neoplasms

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A deep learning framework for efficient analysis of breast volume and fibroglandular tissue using MR data with strong artifacts.

International journal of computer assisted radiology and surgery
PURPOSE: The main purpose of this work is to develop, apply, and evaluate an efficient approach for breast density estimation in magnetic resonance imaging data, which contain strong artifacts including intensity inhomogeneities.

Breast Microcalcification Diagnosis Using Deep Convolutional Neural Network from Digital Mammograms.

Computational and mathematical methods in medicine
Mammography is successfully used as an effective screening tool for cancer diagnosis. A calcification cluster on mammography is a primary sign of cancer. Early researches have proved the diagnostic value of the calcification, yet their performance is...

Predicting breast tumor proliferation from whole-slide images: The TUPAC16 challenge.

Medical image analysis
Tumor proliferation is an important biomarker indicative of the prognosis of breast cancer patients. Assessment of tumor proliferation in a clinical setting is a highly subjective and labor-intensive task. Previous efforts to automate tumor prolifera...

High-Quality Immunohistochemical Stains Through Computational Assay Parameter Optimization.

IEEE transactions on bio-medical engineering
Accurate profiling of tumors using immunohistochemistry (IHC) is essential in cancer diagnosis. The inferences drawn from IHC-stained images depend to a great extent on the quality of immunostaining, which is in turn affected strongly by assay parame...

Comparative assessment of CNN architectures for classification of breast FNAC images.

Tissue & cell
Fine needle aspiration cytology (FNAC) entails using a narrow gauge (25-22 G) needle to collect a sample of a lesion for microscopic examination. It allows a minimally invasive, rapid diagnosis of tissue but does not preserve its histological archite...

RAMS: Remote and automatic mammogram screening.

Computers in biology and medicine
About one in eight women in the U.S. will develop invasive breast cancer at some point in life. Breast cancer is the most common cancer found in women and if it is identified at an early stage by the use of mammograms, x-ray images of the breast, the...

An investigation of the effect of fat suppression and dimensionality on the accuracy of breast MRI segmentation using U-nets.

Medical physics
PURPOSE: Accurate segmentation of the breast is required for breast density estimation and the assessment of background parenchymal enhancement, both of which have been shown to be related to breast cancer risk. The MRI breast segmentation task is ch...

How Should AI Be Developed, Validated, and Implemented in Patient Care?

AMA journal of ethics
Should an artificial intelligence (AI) program that appears to have a better success rate than human pathologists be used to replace or augment humans in detecting cancer cells? We argue that some concerns-the "black-box" problem (ie, the unknowabili...

Automatic Breast and Fibroglandular Tissue Segmentation in Breast MRI Using Deep Learning by a Fully-Convolutional Residual Neural Network U-Net.

Academic radiology
RATIONALE AND OBJECTIVES: Breast segmentation using the U-net architecture was implemented and tested in independent validation datasets to quantify fibroglandular tissue volume in breast MRI.

Evaluating classification accuracy for modern learning approaches.

Statistics in medicine
Deep learning neural network models such as multilayer perceptron (MLP) and convolutional neural network (CNN) are novel and attractive artificial intelligence computing tools. However, evaluation of the performance of these methods is not readily av...