The study of artificial intelligence (AI) has been a continuous endeavor of scientists and engineers for over 65 years. The simple contention is that human-created machines can do …
Obtaining expert labels in clinical imaging is difficult since exhaustive annotation is time- consuming. Furthermore, not all possibly relevant markers may be known and sufficiently …
You are holding in your hands… oh, come on, who holds books like this in their hands anymore? Anyway, you are reading this, and it means that I have managed to release one of …
X Yi, E Walia, P Babyn - Medical image analysis, 2019 - Elsevier
Generative adversarial networks have gained a lot of attention in the computer vision community due to their capability of data generation without explicitly modelling the …
This is a condensed summary of an international multisociety statement on ethics of artificial intelligence (AI) in radiology produced by the ACR, European Society of Radiology, RSNA …
The goals of this review paper on deep learning (DL) in medical imaging and radiation therapy are to (a) summarize what has been achieved to date;(b) identify common and …
Generative adversarial networks (GANs) have become increasingly powerful, generating mind-blowing photorealistic images that mimic the content of datasets they have been …
Generative adversarial networks (GANs) and their extensions have carved open many exciting ways to tackle well known and challenging medical image analysis problems such …
Simple Summary This study investigates the latest achievements, challenges, and future research directions of deep learning techniques for lung cancer and pulmonary nodule …