AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Mammography

Showing 401 to 410 of 615 articles

Clear Filters

SD-CNN: A shallow-deep CNN for improved breast cancer diagnosis.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Breast cancer is the second leading cause of cancer death among women worldwide. Nevertheless, it is also one of the most treatable malignances if detected early. Screening for breast cancer with full field digital mammography (FFDM) has been widely ...

Detection of masses in mammograms using a one-stage object detector based on a deep convolutional neural network.

PloS one
Several computer aided diagnosis (CAD) systems have been developed for mammography. They are widely used in certain countries such as the U.S. where mammography studies are conducted more frequently; however, they are not yet globally employed for cl...

Fuzzy entropy based on differential evolution for breast gland segmentation.

Australasian physical & engineering sciences in medicine
For the diagnosis and treatment of breast tumors, the automatic detection of glands is a crucial step. The true segmentation of the gland is directly related to effective treatment effect of the patient. Therefore, it is necessary to propose an autom...

A review of computer aided detection in mammography.

Clinical imaging
Breast screening with mammography is widely recognized as the most effective method of detecting early breast cancer and has consistently demonstrated a 20-40% decrease in mortality among screened women. Despite this, the sensitivity of mammography r...

Transfer Learning From Convolutional Neural Networks for Computer-Aided Diagnosis: A Comparison of Digital Breast Tomosynthesis and Full-Field Digital Mammography.

Academic radiology
RATIONALE AND OBJECTIVES: With the growing adoption of digital breast tomosynthesis (DBT) in breast cancer screening, we compare the performance of deep learning computer-aided diagnosis on DBT images to that of conventional full-field digital mammog...

Convolutional Neural Network Based Breast Cancer Risk Stratification Using a Mammographic Dataset.

Academic radiology
RATIONALE AND OBJECTIVES: We propose a novel convolutional neural network derived pixel-wise breast cancer risk model using mammographic dataset.

Framework of Computer Aided Diagnosis Systems for Cancer Classification Based on Medical Images.

Journal of medical systems
Early detection of cancer can increase patients' survivability and treatment options. Medical images such as Mammogram, Ultrasound, Magnetic Resonance Imaging, and microscopic images are the common method for cancer diagnosis. Recently, computer-aide...

A fully integrated computer-aided diagnosis system for digital X-ray mammograms via deep learning detection, segmentation, and classification.

International journal of medical informatics
A computer-aided diagnosis (CAD) system requires detection, segmentation, and classification in one framework to assist radiologists efficiently in an accurate diagnosis. In this paper, a completely integrated CAD system is proposed to screen digital...

Classification of Whole Mammogram and Tomosynthesis Images Using Deep Convolutional Neural Networks.

IEEE transactions on nanobioscience
Mammography is the most popular technology used for the early detection of breast cancer. Manual classification of mammogram images is a hard task because of the variability of the tumor. It yields a noteworthy number of patients being called back to...

Minimization of annotation work: diagnosis of mammographic masses via active learning.

Physics in medicine and biology
The prerequisite for establishing an effective prediction system for mammographic diagnosis is the annotation of each mammographic image. The manual annotation work is time-consuming and laborious, which becomes a great hindrance for researchers. In ...