Deep Learning in Healthcare: Paradigms and Applications

✍ Scribed by Yen-Wei Chen, Lakhmi C. Jain

• Publisher: Springer International Publishing
• Year: 2020
• Tongue: English
• Leaves: 225
• Series: Intelligent Systems Reference Library 171
• Edition: 1st ed. 2020
• Category: Library

✦ Synopsis

This book provides a comprehensive overview of deep learning (DL) in medical and healthcare applications, including the fundamentals and current advances in medical image analysis, state-of-the-art DL methods for medical image analysis and real-world, deep learning-based clinical computer-aided diagnosis systems.

Deep learning (DL) is one of the key techniques of artificial intelligence (AI) and today plays an important role in numerous academic and industrial areas. DL involves using a neural network with many layers (deep structure) between input and output, and its main advantage of is that it can automatically learn data-driven, highly representative and hierarchical features and perform feature extraction and classification on one network. DL can be used to model or simulate an intelligent system or process using annotated training data.

Recently, DL has become widely used in medical applications, such as anatomic modelling, tumour detection, disease classification, computer-aided diagnosis and surgical planning. This book is intended for computer science and engineering students and researchers, medical professionals and anyone interested using DL techniques.

✦ Table of Contents

Front Matter ....Pages i-xiv
Front Matter ....Pages 1-1
Medical Image Detection Using Deep Learning (María Inmaculada García Ocaña, Karen López-Linares Román, Nerea Lete Urzelai, Miguel Ángel González Ballester, Iván Macía Oliver)....Pages 3-16
Medical Image Segmentation Using Deep Learning (Karen López-Linares Román, María Inmaculada García Ocaña, Nerea Lete Urzelai, Miguel Ángel González Ballester, Iván Macía Oliver)....Pages 17-31
Medical Image Classification Using Deep Learning (Weibin Wang, Dong Liang, Qingqing Chen, Yutaro Iwamoto, Xian-Hua Han, Qiaowei Zhang et al.)....Pages 33-51
Medical Image Enhancement Using Deep Learning (Yinhao Li, Yutaro Iwamoto, Yen-Wei Chen)....Pages 53-76
Front Matter ....Pages 77-77
Improving the Performance of Deep CNNs in Medical Image Segmentation with Limited Resources (Saeed Mohagheghi, Amir Hossein Foruzan, Yen-Wei Chen)....Pages 79-94
Deep Active Self-paced Learning for Biomedical Image Analysis (Wenzhe Wang, Ruiwei Feng, Xuechen Liu, Yifei Lu, Yanjie Wang, Ruoqian Guo et al.)....Pages 95-110
Deep Learning in Textural Medical Image Analysis (Aiga Suzuki, Hidenori Sakanashi, Shoji Kido, Hayaru Shouno)....Pages 111-126
Anatomical-Landmark-Based Deep Learning for Alzheimer’s Disease Diagnosis with Structural Magnetic Resonance Imaging (Mingxia Liu, Chunfeng Lian, Dinggang Shen)....Pages 127-147
Multi-scale Deep Convolutional Neural Networks for Emphysema Classification and Quantification (Liying Peng, Lanfen Lin, Hongjie Hu, Qiaowei Zhang, Huali Li, Qingqing Chen et al.)....Pages 149-164
Opacity Labeling of Diffuse Lung Diseases in CT Images Using Unsupervised and Semi-supervised Learning (Shingo Mabu, Shoji Kido, Yasuhi Hirano, Takashi Kuremoto)....Pages 165-179
Residual Sparse Autoencoders for Unsupervised Feature Learning and Its Application to HEp-2 Cell Staining Pattern Recognition (Xian-Hua Han, Yen-Wei Chen)....Pages 181-199
Front Matter ....Pages 201-201
Dr. Pecker: A Deep Learning-Based Computer-Aided Diagnosis System in Medical Imaging (Guohua Cheng, Linyang He)....Pages 203-216
Back Matter ....Pages 217-218

✦ Subjects

Engineering; Computational Intelligence; Health Informatics