Anomaly Detection in Medical Image Analysis

2009 ◽  
pp. 426-446 ◽  
Author(s):  
Alberto Taboada-Crispi ◽  
Hichem Sahli ◽  
Denis Hernandez-Pacheco ◽  
Alexander Falcon-Ruiz
Keyword(s):  
Computed Tomography ◽  
Anomaly Detection ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Content Based Image Retrieval ◽  
Computer Assisted ◽  
Future Perspectives ◽  
Brain Computed Tomography ◽  
Computed Tomography Images ◽  
Assisted Diagnosis

Various approaches have been taken to detect anomalies, with certain particularities in the medical image scenario, linked to other terms: content-based image retrieval, pattern recognition, classification, segmentation, outlier detection, image mining, as well as computer-assisted diagnosis, and computeraided surgery. This chapter presents, a review of anomaly detection (AD) techniques and assessment methodologies, which have been applied to medical images, emphasizing their peculiarities, limitations and future perspectives. Moreover, a contribution to the field of AD in brain computed tomography images is also given, illustrated and assessed.

scholarly journals Computer-Assisted Diagnosis System for Breast Cancer in Computed Tomography Laser Mammography (CTLM)

2017 ◽  
Vol 30 (6) ◽  
pp. 796-811 ◽  
Author(s):  
Afsaneh Jalalian ◽  
Syamsiah Mashohor ◽  
Rozi Mahmud ◽  
Babak Karasfi ◽  
M. Iqbal Saripan ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Computed Tomography ◽  
Computer Assisted ◽  
Diagnosis System ◽  
Computer Assisted Diagnosis ◽  
Assisted Diagnosis

scholarly journals Gender imbalance in medical imaging datasets produces biased classifiers for computer-aided diagnosis

2020 ◽  
Vol 117 (23) ◽  
pp. 12592-12594 ◽  
Author(s):  
Agostina J. Larrazabal ◽  
Nicolás Nieto ◽  
Victoria Peterson ◽  
Diego H. Milone ◽  
Enzo Ferrante
Keyword(s):  
Medical Imaging ◽  
Medical Image ◽  
Computer Aided Diagnosis ◽  
Computer Assisted ◽  
Gender Imbalance ◽  
Gender Balance ◽  
Computer Aided ◽  
Medical Image Computing ◽  
Assisted Diagnosis ◽  
Aided Diagnosis

Artificial intelligence (AI) systems for computer-aided diagnosis and image-based screening are being adopted worldwide by medical institutions. In such a context, generating fair and unbiased classifiers becomes of paramount importance. The research community of medical image computing is making great efforts in developing more accurate algorithms to assist medical doctors in the difficult task of disease diagnosis. However, little attention is paid to the way databases are collected and how this may influence the performance of AI systems. Our study sheds light on the importance of gender balance in medical imaging datasets used to train AI systems for computer-assisted diagnosis. We provide empirical evidence supported by a large-scale study, based on three deep neural network architectures and two well-known publicly available X-ray image datasets used to diagnose various thoracic diseases under different gender imbalance conditions. We found a consistent decrease in performance for underrepresented genders when a minimum balance is not fulfilled. This raises the alarm for national agencies in charge of regulating and approving computer-assisted diagnosis systems, which should include explicit gender balance and diversity recommendations. We also establish an open problem for the academic medical image computing community which needs to be addressed by novel algorithms endowed with robustness to gender imbalance.

Computer-assisted detection of metastatic lung tumors on computed tomography

2019 ◽  
Vol 27 (3) ◽  
pp. 199-207
Author(s):  
Yukihiro Yoshida ◽  
Tomoya Sakane ◽  
Jun Isogai ◽  
Yoshio Suzuki ◽  
Soichiro Miki ◽  
...  
Keyword(s):  
Computed Tomography ◽  
False Positive ◽  
Pulmonary Metastases ◽  
Lung Tumors ◽  
Computer Assisted ◽  
Positive Finding ◽  
Computed Tomography Images ◽  
Metastatic Lung ◽  
Computer Assisted Detection ◽  
Benign Nodules

Background This retrospective study examined the performance of computer-assisted detection in the identification of pulmonary metastases. Methods Fifty-five patients (41.8% male) who underwent surgery for metastatic lung tumors in our hospital from 2005 to 2012 were included. Computer-assisted detection software configured to display the top five nodule candidates according to likelihood was applied as the first reader for the preoperative computed tomography images. Results from the software were classified as “metastatic nodule”, “benign nodule”, or “false-positive finding” by two observers. Results Computer-assisted detection identified 85.3% (64/75) of pulmonary metastases that radiologists had detected, and 3 more (4%, 3/75) that radiologists had overlooked. Nodule candidates identified by computer-assisted detection included 86 benign nodules (median size 3.1 mm, range 1.2–18.7 mm) and 121 false-positive findings. Conclusions Computer-assisted detection identified pulmonary metastases overlooked by radiologists. However, this was at the cost of identifying a substantial number of benign nodules and false-positive findings.

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