AIMC Topic: Breast Neoplasms

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Going beyond a First Reader: A Machine Learning Methodology for Optimizing Cost and Performance in Breast Ultrasound Diagnosis.

Ultrasound in medicine & biology
The goal of this study was to devise a machine learning methodology as a viable low-cost alternative to a second reader to help augment physicians' interpretations of breast ultrasound images in differentiating benign and malignant masses. Two indepe...

A similarity-based data warehousing environment for medical images.

Computers in biology and medicine
A core issue of the decision-making process in the medical field is to support the execution of analytical (OLAP) similarity queries over images in data warehousing environments. In this paper, we focus on this issue. We propose imageDWE, a non-conve...

Genomic signatures for paclitaxel and gemcitabine resistance in breast cancer derived by machine learning.

Molecular oncology
Increasingly, the effectiveness of adjuvant chemotherapy agents for breast cancer has been related to changes in the genomic profile of tumors. We investigated correspondence between growth inhibitory concentrations of paclitaxel and gemcitabine (GI5...

Fusing Heterogeneous Features From Stacked Sparse Autoencoder for Histopathological Image Analysis.

IEEE journal of biomedical and health informatics
In the analysis of histopathological images, both holistic (e.g., architecture features) and local appearance features demonstrate excellent performance, while their accuracy may vary dramatically when providing different inputs. This motivates us to...

Sample Selection for Training Cascade Detectors.

PloS one
Automatic detection systems usually require large and representative training datasets in order to obtain good detection and false positive rates. Training datasets are such that the positive set has few samples and/or the negative set should represe...

A risk management model for familial breast cancer: A new application using Fuzzy Cognitive Map method.

Computer methods and programs in biomedicine
Breast cancer is the most deadly disease affecting women and thus it is natural for women aged 40-49 years (who have a family history of breast cancer or other related cancers) to assess their personal risk for developing familial breast cancer (FBC)...

Computational Discrimination of Breast Cancer for Korean Women Based on Epidemiologic Data Only.

Journal of Korean medical science
Breast cancer is the second leading cancer for Korean women and its incidence rate has been increasing annually. If early diagnosis were implemented with epidemiologic data, the women could easily assess breast cancer risk using internet. National Ca...

Breast cancer detection and classification in digital mammography based on Non-Subsampled Contourlet Transform (NSCT) and Super Resolution.

Computer methods and programs in biomedicine
Breast cancer is one of the most perilous diseases among women. Breast screening is a method of detecting breast cancer at a very early stage which can reduce the mortality rate. Mammography is a standard method for the early diagnosis of breast canc...

Robot guidance of an ultrasound probe toward a 3D region of interest detected through X-ray mammography.

International journal of computer assisted radiology and surgery
PURPOSE: This research is situated in the context of breast cancer detection where the standard procedure is the succession of an initial mammography (MX) examination and a supplementary ultrasound (US) scan. One major difficulty of this procedure re...

Expert guided natural language processing using one-class classification.

Journal of the American Medical Informatics Association : JAMIA
INTRODUCTION: Automatically identifying specific phenotypes in free-text clinical notes is critically important for the reuse of clinical data. In this study, the authors combine expert-guided feature (text) selection with one-class classification fo...