Computer-aided diagnosis in medical imaging: historical review, current status and future potential

K Doi - Computerized medical imaging and graphics, 2007 - Elsevier
Computer-aided diagnosis (CAD) has become one of the major research subjects in
medical imaging and diagnostic radiology. In this article, the motivation and philosophy for …

Computer-aided breast cancer detection using mammograms: a review

K Ganesan, UR Acharya, CK Chua… - IEEE Reviews in …, 2012 - ieeexplore.ieee.org
The American Cancer Society (ACS) recommends women aged 40 and above to have a
mammogram every year and calls it a gold standard for breast cancer detection. Early …

Psychological science can improve diagnostic decisions

JA Swets, RM Dawes… - Psychological science in …, 2000 - journals.sagepub.com
Diagnostic problems abound for individuals, organizations, and society. The stakes are high,
often life and death. Such problems are prominent in the fields of health care, public safety …

Machine learning in medical imaging

MN Wernick, Y Yang, JG Brankov… - IEEE signal …, 2010 - ieeexplore.ieee.org
This article will discuss very different ways of using machine learning that may be less
familiar, and we will demonstrate through examples the role of these concepts in medical …

Breast image analysis for risk assessment, detection, diagnosis, and treatment of cancer

ML Giger, N Karssemeijer… - Annual review of …, 2013 - annualreviews.org
The role of breast image analysis in radiologists' interpretation tasks in cancer risk
assessment, detection, diagnosis, and treatment continues to expand. Breast image analysis …

New frontiers: an update on computer-aided diagnosis for breast imaging in the age of artificial intelligence

Y Gao, KJ Geras, AA Lewin… - American Journal of …, 2019 - Am Roentgen Ray Soc
OBJECTIVE. The purpose of this article is to compare traditional versus machine learning–
based computer-aided detection (CAD) platforms in breast imaging with a focus on …

A study on several machine-learning methods for classification of malignant and benign clustered microcalcifications

L Wei, Y Yang, RM Nishikawa… - IEEE transactions on …, 2005 - ieeexplore.ieee.org
In this paper, we investigate several state-of-the-art machine-learning methods for
automated classification of clustered microcalcifications (MCs). The classifier is part of a …

Noise injection for training artificial neural networks: A comparison with weight decay and early stopping

RM Zur, Y Jiang, LL Pesce, K Drukker - Medical physics, 2009 - Wiley Online Library
The purpose of this study was to investigate the effect of a noise injection method on the
“overfitting” problem of artificial neural networks (ANNs) in two‐class classification tasks. The …

Improving breast cancer diagnosis with computer-aided diagnosis

Y Jiang, RM Nishikawa, RA Schmidt, CE Metz… - Academic radiology, 1999 - Elsevier
RATIONALE AND OBJECTIVES.: The purpose of this study was to test whether computer-
aided diagnosis (CAD) can improve radiologists' performance in breast cancer diagnosis …

A receiver operating characteristic partial area index for highly sensitive diagnostic tests.

Y Jiang, CE Metz, RM Nishikawa - Radiology, 1996 - pubs.rsna.org
PURPOSE: Area under a receiver operating characteristic (ROC) curve (Az) is widely used
as an index of diagnostic performance. However, Az is not a meaningful summary of clinical …