Deep Learning in Medical Imaging

Abstract

The artificial neural network (ANN), one of the machine learning (ML) algorithms, inspired by the human brain system, was developed by connecting layers with artificial neurons. However, due to the low computing power and insufficient learnable data, ANN has suffered from overfitting and vanishing gradient problems for training deep networks. The advancement of computing power with graphics processing units and the availability of large data acquisition, deep neural network outperforms human or other ML capabilities in computer vision and speech recognition tasks. These potentials are recently applied to healthcare problems, including computer-aided detection/diagnosis, disease prediction, image segmentation, image generation, etc. In this review article, we will explain the history, development, and applications in medical imaging

Detection and Diagnosis of Breast Cancer Using Artificial Intelligence Based Assessment of Maximum Intensity Projection Dynamic Contrast-Enhanced Magnetic Resonance Images

“…In this study, we used RetinaNet, a state-of-the-art one-stage object detector that has been successfully applied to medical images such as mammography, computed tomography, and x-ray examinations [12,26]. We have shown that it is useful for detecting and diagnosing breast lesions on MIPs of DCE breast MRI.…”

The use of artificial intelligence, machine learning and deep learning in oncologic histopathology

“…In medicine, the most prominent application of deep learning has been its use in the detection of abnormalities or disease in radiology and pathology. 3,4 Deep learning-based pattern-recognition applications have been effective in analyzing large medical images and can accomplish many tasks including object detection (identifying the location of the lesion of interest) and image classification (generating differential diagnoses). 3…”

Allosteric Regulation at the Crossroads of New Technologies: Multiscale Modeling, Networks, and Machine Learning

“…The remarkable rise of deep learning (DL) relying on the robust function approximations and representation properties of deep neural networks has provided us with new tools to automatically find compact low-dimensional representations (features) of high-dimensional data (LeCun et al, 2015 ). DL models have achieved outstanding predictive performance making dramatic breakthroughs in a wide range of applications, including automatic speech processing and image recognition (Toledano et al, 2018 ; Kim et al, 2019 ; Hey et al, 2020 ; Wu et al, 2020 ). In the words of Geoffrey Hinton who is the founder of DL technologies “Deep Learning is an algorithm which has no theoretical limitations on what it can learn; the more data you give and the more computational time you provide the better it is” (LeCun et al, 2015 ).…”