CAD and AI for breast cancer-recent development and challenges.
Journal:
The British journal of radiology
Published Date:
Dec 16, 2019
Abstract
Computer-aided diagnosis (CAD) has been a popular area of research and development in the past few decades. In CAD, machine learning methods and multidisciplinary knowledge and techniques are used to analyze the patient information and the results can be used to assist clinicians in their decision making process. CAD may analyze imaging information alone or in combination with other clinical data. It may provide the analyzed information directly to the clinician or correlate the analyzed results with the likelihood of certain diseases based on statistical modeling of the past cases in the population. CAD systems can be developed to provide decision support for many applications in the patient care processes, such as lesion detection, characterization, cancer staging, treatment planning and response assessment, recurrence and prognosis prediction. The new state-of-the-art machine learning technique, known as deep learning (DL), has revolutionized speech and text recognition as well as computer vision. The potential of major breakthrough by DL in medical image analysis and other CAD applications for patient care has brought about unprecedented excitement of applying CAD, or artificial intelligence (AI), to medicine in general and to radiology in particular. In this paper, we will provide an overview of the recent developments of CAD using DL in breast imaging and discuss some challenges and practical issues that may impact the advancement of artificial intelligence and its integration into clinical workflow.
Authors
Keywords
Artificial Intelligence
Bibliometrics
Breast Neoplasms
Decision Support Systems, Clinical
Deep Learning
Diagnosis, Computer-Assisted
Female
Humans
Image Interpretation, Computer-Assisted
Magnetic Resonance Imaging
Mammography
Neural Networks, Computer
Quality Assurance, Health Care
Radiology
Ultrasonography, Mammary