Deep convolutional neural networks for mammography: advances, challenges and applications
BACKGROUND: The limitations of traditional computer-aided detection (CAD) systems for mammography, the extreme importance of early detection of breast cancer and the high impact of the false diagnosis of patients drive researchers to investigate deep learning (DL) methods for mammograms (MGs). Recent breakthroughs in DL, in particular, convolutional neural networks (CNNs) have achieved remarkable advances in the medical fields. Specifically, CNNs are used in mammography for lesion localization a
Record details
Published: 1 June 2019
Source: OpenAlex
Category: Research
Topics: Healthcare · Finance, VC & PE
Retrieved: 14 July 2026
Related evidence
These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.
FDA-ARGOS is a database with public quality-controlled reference genomes for diagnostic use and regulatory science
OpenAlex · 25 July 2019
Association Between Surgical Skin Markings in Dermoscopic Images and Diagnostic Performance of a Deep Learning Convolutional Neural Network for Melanoma Recognition
OpenAlex · 15 August 2019
Physicians’ Perceptions of Chatbots in Health Care: Cross-Sectional Web-Based Survey
OpenAlex · 9 February 2019
A governance model for the application of AI in health care
OpenAlex · 10 October 2019
Assessment of Accuracy of an Artificial Intelligence Algorithm to Detect Melanoma in Images of Skin Lesions
OpenAlex · 16 October 2019
Digital Mental Health and COVID-19: Using Technology Today to Accelerate the Curve on Access and Quality Tomorrow
OpenAlex · 26 March 2020
How to cite this record
ethics.ai (1 June 2019), “Deep convolutional neural networks for mammography: advances, challenges and applications,” evidence record 8608, https://ethics.ai/record/8608 (originally published by OpenAlex).
Use and limitations
This page is a stable index and citation surface for a source record. ethics.ai did not author the underlying report and has not independently verified every claim. Automatic topics may be imperfect. For consequential use, quote and cite the original publisher.