Autoencoder-based part clustering for part-in-whole retrieval of CAD models

Muraleedharan, Lakshmi Priya; Kannan, Shyam Sundar; Muthuganapathy, Ramanathan

doi:10.1016/j.cag.2019.03.016

Cited by 14 publications

(1 citation statement)

References 31 publications

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“…Autoencoder is a subclass of neural networks that can learn to reconstruct data from compressed versions of themselves and, thus, mainly used for representation learning [69]. They are widely used in imaged-based CAD for unsupervised learning [79,80], self-supervised learning [81], and denoising [82].…”

Section: Deep Learningmentioning

confidence: 99%

Advances of AI in Image-Based Computer-Aided Diagnosis: A Review

Yeasmin

2023

Preprint

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Over the past two decades, computer-aided detection or diagnosis has emerged as a highly promising field of research. Its primary goal is to enhance the diagnostic and treatment procedures for radiologists and clinicians in medical image analysis. With the help of big data and advanced Artificial Intelligence (AI) technologies, i.e., machine learning and deep learning algorithms, assist in making our healthcare system more convenient, active, efficient, and personalized. The primary goal of the literature survey study is to present a thorough overview of the most important developments related to Computer-Aided Diagnosis (CAD) systems in medical imaging. This survey holds considerable importance for researchers and professionals in both medical and computer science. Several reviews regarding specific facets of CAD in medical imaging already exist. Nevertheless, the main emphasis of this paper is on covering the complete range of CAD systems’ capabilities in medical imaging. This review article introduces the background concept used for typical CAD systems in medical imaging by outlining and comparing several methods frequently employed in recent studies. The article also offers a comprehensive and well-structured survey of CAD in medicine, drawing from a meticulous selection of relevant publications. Moreover, it describes the process of handling medical images and introduces state-of-the-art AI-based CAD technologies in medical imaging, along with the future directions of CAD. This study indicates that deep learning algorithms are the most effective way to diagnose or detect diseases.

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