Deep Learning for Computer-aided Medical Diagnosis

Submission Deadline: 01 March 2019

IEEE Access invites manuscript submissions in the area of Deep Learning for Computer-aided Medical Diagnosis.

With the growing popularity of neuroimaging scanners in hospitals and institutes, the tasks of radiologists are increasing. The manual interpretation suffers from inter- and intra-radiologist variance. In addition, emotion, fatigue, and other factors will influence the manual interpretation result.

Computer-aided medical diagnosis (CAMD) are procedures in medicine to assist radiologists and doctors in the interpretation of medical images, which may come from CT, X-ray, ultrasound, thermography, MRI, PET, SPECT, etc. In practical situations, CAMD can help radiologists interpret a medical image within seconds.

Conventional CAMD tools are built on top of handcrafted features. Recent progress on deep learning opens a new era that can automatically build features from the large amount of data. On the other hand, many important medical projects were launched during the last decade (Human brain project, Blue brain project, Brain Initiative, etc.) that provides massive data. Those emerging big medical data can support the use of deep learning.

This Special Section in IEEE Access aims to provide a forum to present the latest advancements in deep learning research that directly concerns the computer-aided diagnosis community. It is especially important to develop deep networks to capture normal-appearing lesions, which may be neglected by human interpretation.

The topics of interest include, but are not limited to:

• CAMD for neurodegenerative diseases, neoplastic disease, cerebrovascular disease, and inflammatory disease.
• Deep learning and regularization techniques (Multi-task learning, autoencoder, sparse representation, dropout, batch normalization, convolutional neural network, transfer learning, etc.)
• Novel training and inference methods for deep networks
• Deep network architecture for CAMD and big medical data
• Deep learning for cancer location, cancer image segmentation, cancer tissue classification, cancer image retrieval
• Other medical signal and image processing related applications.

We also highly recommend the submission of multimedia with each article as it significantly increases the visibility, downloads, and citations of articles.

Associate Editor: Yu-Dong Zhang, University of Leicester, UK

Guest Editors:

1. Zhengchao Dong, Columbia University, USA
2. Carlo Cattani, University of Tuscia, Italy
3. Shui-Hua Wang, Nanjing Drum Tower Hospital, China

Relevant IEEE Access Special Sections:

1. New Trends in Brain Signal Processing and Analysis
2. Advanced Information Sensing and Learning Technologies for Data-centric Smart Health Applications
3. Data Mining and Granular Computing in Big Data and Knowledge Processing

IEEE Access Editor-in-Chief: Michael Pecht, Professor and Director, CALCE, University of Maryland

Paper submission: Contact Associate Editor and submit manuscript to:
http://ieee.atyponrex.com/journal/ieee-access

For inquiries regarding this Special Section, please contact: yudongzhang@ieee.org