Titlebook: Data Driven Approaches on Medical Imaging; Bin Zheng,Stefan Andrei,Kishor Datta Gupta Book 2023 The Editor(s) (if applicable) and The Auth

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https://doi.org/10.1007/978-3-031-07357-1ata augmentation. GANs work by employing a generator network to create new data samples that are then assessed by a discriminator network to determine their similarity to real samples. The discriminator network is taught to differentiate between actual and synthetic samples, while the generator syst

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https://doi.org/10.1007/978-3-031-07357-1ment of diseases. However, there are several ethical concerns associated with medical imaging research, including bias and explainability of the decision-making process. In this chapter, we will discuss these concerns and how they can be addressed in medical imaging research. Bias refers to the syst

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https://doi.org/10.1007/978-3-031-07357-1ssues and organs of the body. These images provide vital information for clinical diagnosis, and in this chapter, we will explore the use of X-ray, MRI, and nuclear imaging in detecting severe illnesses. However, manual evaluation and storage of these images can be a challenging and time-consuming p

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different medical imaging system.highlight challenges in dev.This book deals with the recent advancements in computer vision techniques such as active learning, few-shot learning, zero shot learning, explainable and interpretable ML, online learning, AutoML etc. and their applications in medical dom

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https://doi.org/10.1007/978-3-031-07357-1e used to develop new quantitative image markers and prediction models for disease detection, diagnosis, and prognosis prediction. By covering the basic knowledge of medical imaging informatics, this chapter provides a foundation for understanding the role of informatics in medicine and its potential impact on patient care.

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https://doi.org/10.1007/978-3-031-07357-1-invasively create images of internal organs and body parts for diagnostic and procedural purposes. This article aims to highlight the potential applications, strategies, and techniques of AutoML in medical imaging through theoretical and empirical evidence.

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https://doi.org/10.1007/978-3-031-07357-1ce medical image analysis outcomes. This study provides an overview of IST in medical imaging by considering the types of IST, their application, limitations, and potential scopes for future researchers and practitioners.

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Invariant Scattering Transform for Medical Imaging,ce medical image analysis outcomes. This study provides an overview of IST in medical imaging by considering the types of IST, their application, limitations, and potential scopes for future researchers and practitioners.

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