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DLCAMD 2018 : Deep Learning for Computer-aided Medical Diagnosis (Multimedia Tools and Applications)

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Link: http://static.springer.com/sgw/documents/1632557/application/pdf/MTAP+CFP+1119.pdf
 
When N/A
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Submission Deadline Oct 1, 2018
Categories    deep learning   autoencoder   transfer learning   computer-aided diagnosis
 

Call For Papers

Special Issue on
Deep Learning for Computer-aided Medical Diagnosis

Scopes and Objectives


As the growing popularity of neuroimaging scanners in hospitals and institutes, the tasks of radiologists are apparently increasing. This manual interpretation suffers from inter- and intra-radiologist variance. Besides, the emotion and fatigue and other factors will influence the manual interpretation result.
Computer-aided medical diagnosis (CAMD) are procedures in medicine that 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, CAMD can help radiologists to interpret the 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 issue aims to provide a forum to present latest advancements in deep learning research that directly concerns the computer-aided diagnosis community. It is especially important to develop deep networks to capture the normal-appearing lesions, which may be neglected by human interpretation.

Topics 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, 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.

Submission Guideline

Submitted papers should present original, unpublished work, relevant to one of the topics of the Special Issue. All submitted papers will be evaluated on the basis of relevance, significance of contribution, technical quality, scholarship, and quality of presentation, by at least three independent reviewers. It is the policy of the journal that no submission, or substantially overlapping submission, be published or be under review at another journal or conference at any time during the review process.
Prospective authors are invited to submit their papers directly via the online submission system at: https://www.editorialmanager.com/mtap/. Choosing “1119 - Deep Learning for Computer-aided Medical Diagnosis” as article type. When uploading your paper, please ensure that your manuscript is marked as being for this special issue.

Important Dates

Submission deadline: Oct/2018

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