AIMC Topic: Breast

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Deep Convolutional Neural Networks-Based Automatic Breast Segmentation and Mass Detection in DCE-MRI.

Computational and mathematical methods in medicine May 5, 2020
Breast segmentation and mass detection in medical images are important for diagnosis and treatment follow-up. Automation of these challenging tasks can assist radiologists by reducing the high manual workload of breast cancer analysis. In this paper,...
Breast Neoplasms Female Middle Aged Aged Computational Biology Young Adult Contrast Media Magnetic Resonance Imaging Adult Humans Breast Neural Networks, Computer Databases, Factual Image Interpretation, Computer-Assisted
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Review of the current state of digital image analysis in breast pathology.

The breast journal Apr 27, 2020
Advances in digital image analysis have the potential to transform the practice of breast pathology. In the near future, a move to a digital workflow offers improvements in efficiency. Coupled with artificial intelligence (AI), digital pathology can ...
Female Reproducibility of Results Breast Neoplasms Breast Humans Artificial Intelligence
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Deep learning assessment of breast terminal duct lobular unit involution: Towards automated prediction of breast cancer risk.

PloS one Apr 15, 2020
Terminal duct lobular unit (TDLU) involution is the regression of milk-producing structures in the breast. Women with less TDLU involution are more likely to develop breast cancer. A major bottleneck in studying TDLU involution in large cohort studie...
Adult Breast Neoplasms Humans Biopsy Deep Learning Cohort Studies Image Processing, Computer-Assisted Age Factors Breast Female Reproducibility of Results Risk Factors Middle Aged Risk Assessment
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A machine learning approach on multiscale texture analysis for breast microcalcification diagnosis.

BMC bioinformatics Mar 11, 2020
BACKGROUND: Screening programs use mammography as primary diagnostic tool for detecting breast cancer at an early stage. The diagnosis of some lesions, such as microcalcifications, is still difficult today for radiologists. In this paper, we proposed...
Humans Breast Neoplasms ROC Curve Calcinosis Mammography Machine Learning Algorithms Databases, Factual Female Area Under Curve Breast
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Improving breast mass classification by shared data with domain transformation using a generative adversarial network.

Computers in biology and medicine Mar 10, 2020
Training of a convolutional neural network (CNN) generally requires a large dataset. However, it is not easy to collect a large medical image dataset. The purpose of this study is to investigate the utility of synthetic images in training CNNs and to...
Mammography Area Under Curve Breast Neural Networks, Computer Tomography, X-Ray Computed
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Can a machine-learning model improve the prediction of nodal stage after a positive sentinel lymph node biopsy in breast cancer?

Acta oncologica (Stockholm, Sweden) Mar 9, 2020
The current standard for evaluating axillary nodal burden in clinically node negative breast cancer is sentinel lymph node biopsy (SLNB). However, the accuracy of SLNB to detect nodal stage N2-3 remains debatable. Nomograms can help the decision-mak...
Sentinel Lymph Node Biopsy Lymph Node Excision Axilla Regression Analysis Breast Tumor Burden Carcinoma, Ductal, Breast Sentinel Lymph Node Biopsy, Fine-Needle Humans Lymph Nodes Machine Learning Nomograms Retrospective Studies Lymphatic Metastasis Middle Aged Female Aged Neoplasm Invasiveness ROC Curve Logistic Models Aged, 80 and over Adult Breast Neoplasms
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Deep learning radiomics can predict axillary lymph node status in early-stage breast cancer.

Nature communications Mar 6, 2020
Accurate identification of axillary lymph node (ALN) involvement in patients with early-stage breast cancer is important for determining appropriate axillary treatment options and therefore avoiding unnecessary axillary surgery and complications. Her...
Neoplasm Staging Breast Mastectomy Reference Standards Lymph Nodes Aged, 80 and over Lymphatic Metastasis Lymph Node Excision Axilla Adult Preoperative Period Breast Neoplasms Elasticity Imaging Techniques Middle Aged Deep Learning Ultrasonography Prospective Studies ROC Curve Humans Prognosis Image Processing, Computer-Assisted Female Aged
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A convolutional neural network-based model observer for breast CT images.

Medical physics Feb 29, 2020
PURPOSE: In this paper, we propose a convolutional neural network (CNN)-based efficient model observer for breast computed tomography (CT) images.
Least-Squares Analysis Breast Signal-To-Noise Ratio Neural Networks, Computer Normal Distribution Tomography, X-Ray Computed Image Processing, Computer-Assisted
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Tabu Search and Machine-Learning Classification of Benign and Malignant Proliferative Breast Lesions.

BioMed research international Feb 27, 2020
Breast cancer is the most diagnosed cancer among women around the world. The development of computer-aided diagnosis tools is essential to help pathologists to accurately interpret and discriminate between malignant and benign tumors. This paper prop...
Image Interpretation, Computer-Assisted Fibrocystic Breast Disease Neoplasms Diagnosis, Computer-Assisted Cell Proliferation Breast Algorithms Female Humans Breast Neoplasms Machine Learning
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External Validation of a Deep Learning Model for Predicting Mammographic Breast Density in Routine Clinical Practice.

Academic radiology Feb 20, 2020
RATIONALE AND OBJECTIVES: Federal legislation requires patient notification of dense mammographic breast tissue because increased density is a marker of breast cancer risk and can limit the sensitivity of mammography. As previously described, we clin...
Humans Breast Neoplasms Deep Learning Mammography Breast Breast Density
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