Analysis of the impact of digital watermarking on computer-aided diagnosis in medical imaging

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.

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Artificial intelligence, machine learning, computer-aided diagnosis, and radiomics: advances in imaging towards to precision medicine

Radiologia Brasileira ◽

10.1590/0100-3984.2019.0049 ◽

2019 ◽

Vol 52 (6) ◽

pp. 387-396 ◽

Cited By ~ 9

Author(s):

Marcel Koenigkam Santos ◽

José Raniery Ferreira Júnior ◽

Danilo Tadao Wada ◽

Ariane Priscilla Magalhães Tenório ◽

Marcello Henrique Nogueira Barbosa ◽

...

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Diagnostic Imaging ◽

Precision Medicine ◽

Quantitative Information ◽

Computer Aided Diagnosis ◽

Rapid Expansion ◽

Computer Aided ◽

The Impact ◽

Aided Diagnosis

Abstract The discipline of radiology and diagnostic imaging has evolved greatly in recent years. We have observed an exponential increase in the number of exams performed, subspecialization of medical fields, and increases in accuracy of the various imaging methods, making it a challenge for the radiologist to “know everything about all exams and regions”. In addition, imaging exams are no longer only qualitative and diagnostic, providing now quantitative information on disease severity, as well as identifying biomarkers of prognosis and treatment response. In view of this, computer-aided diagnosis systems have been developed with the objective of complementing diagnostic imaging and helping the therapeutic decision-making process. With the advent of artificial intelligence, “big data”, and machine learning, we are moving toward the rapid expansion of the use of these tools in daily life of physicians, making each patient unique, as well as leading radiology toward the concept of multidisciplinary approach and precision medicine. In this article, we will present the main aspects of the computational tools currently available for analysis of images and the principles of such analysis, together with the main terms and concepts involved, as well as examining the impact that the development of artificial intelligence has had on radiology and diagnostic imaging.

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An Overview of Volume Rendering Techniques for Medical Imaging

International Journal of Online and Biomedical Engineering (iJOE) ◽

10.3991/ijoe.v16i06.13627 ◽

2020 ◽

Vol 16 (06) ◽

pp. 95

Author(s):

Amr S. Mady ◽

Samir Abou El-Seoud

Keyword(s):

Medical Imaging ◽

Volume Rendering ◽

Computer Aided Diagnosis ◽

Use Cases ◽

Methods And Techniques ◽

Computer Aided ◽

Pros And Cons ◽

Aided Diagnosis ◽

Rendering Techniques

One of the most valuable medical imaging visualizations or computer-aided diagnosis is Volume rendering (VR). This survey’s objective is reviewing and comparing between several methods and techniques of VR, for a better and more comprehensive reading and learning of both pros and cons of each method, and their use cases.

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